But with all the hype, how can marketing leaders and their teams best determine how to utilize AI to benefit their work and customers? This four-part series will explore four categories of artificial intelligence (AI) and how they can meaningfully impact marketers and their customers. 

This first article of the series will look at generative AI, a category that includes tools you might have used — or at least heard plenty about — like OpenAI’s ChatGPT or Google’s Bard (focused on generating text responses to queries), MidJourney and Dall-E (focused on generating imagery) and many others. This category may be very familiar to you, but we’ll take a fresh look at it here before moving on to other uses of AI in the subsequent parts of this series.

What is generative AI?

Generative AI uses existing text, imagery and other information and creates new content from those sources. They use underlying technologies like GPT (Generative Pre-trained Transformer, used for text generation) or Stable Diffusion (used for image generation). They are trained on specific or more general sets of data and information. 

Some commonly used generative AI tools include:

• ChatGPT (text)
• DALL-E and MidJourney (image)

This category of AI requires the user to provide a prompt to generate output. It is important to note that everything generated is based on some prior learning or source text, images or other materials, and the base technology continues to evolve. That, combined with the materials that the tools are “trained” on, determines the quality of the output. 

Why it’s worth paying attention to 

While generative AI is still in its infancy, it can still be a great source of new ideas. Think of it as an idea generator that provides the foundation for creative work. By asking ChatGPT for 10 ideas for blog posts, you might get 3-4 solid ideas (meaning 6-7 will get discarded), but that is still quicker and easier than brainstorming when you are on a deadline.

Another way of thinking of generative AI is as a starting point for tasks, knowing that what you are getting is preliminary to even a first draft. But, if you know that going in, the tool is only meant to cure writer’s block (in the case of text-based content) or give your illustrators or designers a rough idea of the visuals you have in mind. 

Finally, generative AI is beginning to be used in the context of personalization. Looking at it as a pure personalization engine is still a bit of a stretch, but there are some interesting use cases within limitations. 

Text-based generation of personalized variations and image-based ones that restrict copyrighted images and brand-safe elements are already being used, at least in limited ways. This area may have the most potential because of the prohibitive scaling issues associated with creating content required for hyper-personalization. 

Near-term potential

Although there is still much room for improvement, marketers can look forward to a couple of things in the (hopefully) near future.

Using it as an idea generator or as a starting point for creative content presents nearly limitless opportunities. It is one of the reasons you have probably heard so much about generative AI recently, as ChatGPT has also become the most quickly adopted software platform of all time. 

Greater personalization opportunities will also continue to be available, particularly as the quality of results and the ability to restrict specific source material and keep things “brand safe” becomes more sophisticated. 

What to watch out for

Generative AI is arguably the most-hyped flavor of artificial intelligence tools at the moment. That said, it is far from perfect, and there are some areas that marketers and others that wish to utilize it should watch out for.

First, there are easily avoidable errors that generative AI can sometimes introduce into otherwise compelling or interesting text or visuals. For instance, generating images of humans with four fingers or in impossible poses made some early examples of this type of AI not quite ready for prime time.

While the image tools have improved and are making these errors less often, there are still other things you will want to watch out for that might be more nuanced. These might include the background imagery or anonymous people in a crowd with odd errors.

Another potentially larger issue is the rights of the images used as source material. Because generative AI bases its responses to prompts on existing materials — essentially whatever it was “trained” on — the sources of those materials matter greatly. Because of this, there can be plagiarism and copyright issues if care is not taken to avoid using copyrighted images as source material. 

We have already seen some lawsuits around this, and the potential for more exists. 

Conclusion

As you can see, generative AI has some compelling current uses and holds considerable potential, but marketers should also proceed with caution before wholesale adoption. 

In the next article in this series, we will look at another area where AI can play an impactful role in marketing: predictive analytics. 

The post The 4 categories of AI that impact marketing: Generative AI appeared first on MarTech.

There are ways to use additional solutions, notably artificial intelligence and journey orchestration, to take your existing marketing automation approaches to the next level. 

Challenges with legacy marketing automation

Marketers have achieved amazing results with marketing automation in the past. But customers’ shifting expectations and behaviors are pushing traditional approaches to their limits. Brands that rely on marketing automation alone find engaging with active customers on multiple channels difficult. 

Personalization in most marketing automation platforms (MAPs) is limited to simple rules-based instructions (“if this, then that”). For example, if a customer abandons their shopping cart, send them a reminder email. Or, if a customer signed up for an email list, send them a welcome message. 

This approach doesn’t allow for complex variations based on the segment a customer might be in, their propensity to buy, their past individual behavior, or other factors. While some MAPs can technically achieve this, building all rule sets can make ongoing management nearly impossible and fraught with errors based on cascading dependencies. 

As customers demand more dynamic experiences, your marketing automation approach must be augmented. This is where customer journey orchestration and artificial intelligence (AI) come in. 

How journey orchestration augments marketing automation

Traditional marketing automation can still work well in providing timely and relevant communications when dealing with a single response channel such as email. But your customers don’t rely on a single channel to interact. Instead, they are channel-switching and expect brands to keep up. 

Additionally, most marketing automation workflows are generally simple and rarely incorporate complex branching, particularly across multiple channels. 

This is where customer journey orchestration (CJO) can save the day. CJO was built for multi-channel communication. Thus, emails, SMS messages, mobile app push notifications, website landing pages and social advertising can be hyper-targeted.

Through branching, a series of actions can result in vast differences for one customer versus another, even if they are enrolled in the same journey. 

This way, CJO boosts your marketing automation, aligning with your customers’ growing expectations about receiving content, offers and experiences when, where and how they want them. 

Dig deeper: What is customer journey orchestration and how does it work?

The impact of AI on marketing automation

Since most marketing automation relies on a simpler, rules-based approach, AI plays a key role in augmenting these decisions in coordination with or separate from customer journey orchestration. 

Below are some ways AI can take your marketing efforts beyond your current marketing automation methods. 

Dynamic segmentation

Finding patterns in vast amounts of structured and unstructured data is cumbersome for humans. AI, on the other hand, can do this well. AI-based machine learning algorithms help you glean audience insights, turning formerly static audience segments into more dynamic ones.

These dynamic segments can group customers based on buying patterns or behavior that enable personalized content, offers and experiences to reach the right person at the right time on the right channel. That’s marketing gold. 

Propensity calculation and predictive analytics 

Adding AI capabilities can significantly enhance your performance if your MAP uses fairly simple if/then logic to send messages and take action. You can use propensity scoring and predictive analytics to route the next best action or offer to an individual. 

Personalized content generation 

The promise of generative AI tools is that content, including text and imagery, can be tailored on a one-to-one basis for the individual rather than everyone in a large audience segment receiving the same thing.

While there are hurdles to be overcome in this area, truly personalized content and images can lead to greater relevance, loyalty and lifetime value.

Dig deeper: How AI can help your marketing right now

What needs to change to be successful 

To successfully augment marketing automation with customer journey orchestration and AI, marketers must remember these three impactful points. 

Personalized journeys

Ensure your approach is customer-centric. Consider the experience an individual may want versus a one-size-fits-all approach that lumps many customers into the same buckets.

While a segmented approach is better than sending the same message on the same channel to everyone, it still doesn’t individualize the timing, channel, content and offers nearly enough to meet rising consumer expectations. 

Integrations

Better integrations can have a positive impact when using journey orchestration and AI to augment your marketing automation. 

Platform integrations

To take advantage of a true multi-channel customer journey orchestration approach, you will need to integrate several platforms in ways they most likely have not been integrated. Fortunately, because this is a core functionality of CJO tools, this can be straightforward in most instances. But, as with any integration, there can always be unexpected hitches. 

A common way to start is to use an iterative approach that builds toward an omnichannel setup, one system and platform integration at a time. This makes it easier and quicker to implement the first integration, letting your teams learn more quickly to become more efficient and produce effective results as time progresses. 

Data integrations

Platform integrations will also require your data sources to be more unified. You must ensure you are:

• Reaching the right customer on their platform of choice.
• Informed by the relevant purchase and behavioral data across multiple internal systems and platforms.
• Augmented by other demographic or psychographic information that may be beneficial. 

As you may well know, integrating all these data sources can be daunting for even the most sophisticated organizations. 

Team integrations 

Just as platforms and data can be siloed, your marketing teams can often be just as disconnected. Your email marketing team needs to coordinate with your mobile app team and SMS team and so forth throughout the organization. 

Doing CJO well and using AI to the fullest means that teams work together from the initial campaign and content creation to the measurement and optimization of your collective efforts. 

Omnichannel approach 

Finally, it is time to evolve your customer experience approach toward omnichannel and customer-centric instead of reactive and marketing-driven. Customer journey orchestration was built to be multichannel, and AI applications thrive in processing structured and unstructured data sources.

Improving upon your marketing automation means keeping these approaches in mind as you find more effective ways to provide tailored customer experiences. 

Conclusion

Using customer journey orchestration and artificial intelligence-based approaches to augment your current marketing automation efforts can dramatically impact your ability to deliver valuable experiences for your customers and your brand. 

The post AI powered marketing automation: How to make it work for you appeared first on MarTech.

It takes a fully-functioning team across multiple disciplines to create successful marketing results. While an individual or two may stand out, the collective strategic work is what achieves winning and sustainable ROI. Long-term success depends on supportive leadership and nimble teams that keep up with changing times while building valuable lifetime customers. 

The final part of this series tackles what an agile, customer-centric culture means, why leadership support matters and how organizations can work toward this goal.

The components of an agile, customer-centric culture

Culture

Whereas values are principles and goals that guide strategies and are often aspirational, culture is the outcome of shared values, goals and practices within an organization.

In other words, while values are how a company describes its ideals and standards for interacting with customers and employees, culture is the way work is actually done. When the pressure is high or priorities are competing, culture is how work gets prioritized, performed and evaluated. 

Culture isn’t static, either. How work gets done — or needs to get done — may shift depending on where a company is in its growth and maturity or the nature of competition or innovation in its sector. 

For instance, a startup may benefit from an extremely collaborative and highly innovative culture in its early stages. Over time, additional structure and a stronger focus on sales and competitive marketing may be needed. Thus, while an organization’s founding values may not change, how those values are interpreted and practiced may need to evolve for practical or strategic reasons. 

Becoming customer-centric

Customer-centricity is an organization’s continual focus on improving the customer experience, with a shared understanding from the top down and bottom up that doing so will improve business performance. This means every employee can see their role in serving and creating better customer experiences.

But being customer-centric doesn’t mean the company doesn’t care about its employees. The most successful companies find a way to do both in a manner that reinforces each other. Employees are motivated to create great customer experiences and are rewarded when they achieve that.

Thus, the foundational element of customer-centricity ensures everyone is aligned with the customer’s interests. By doing so, the company succeeds when customers have a better experience and buy more or often.

Agility in company culture

You may be familiar with Agile as a set of principles and practices for software engineering or even marketing work in organizations. Scrum and Kanban are often used to organize and manage project work in iterative cycles (or the Scaled Agile Framework at the organizational level). While it makes sense for projects and ongoing marketing activities, what exactly is an agile culture? 

An agile culture means keeping an openness to change and an appreciation for making decisions subject to evaluation and evolution. It can take different forms, but one common earmark is encouraging experimentation and learnings over fear that everything must be right on the first try. This openness to learning and willingness to accept failure to gain insights may seem strange to some. Still, organizations that embrace such culture can move rapidly because every misstep is a learning moment.

Thus, an agile culture is built for flexibility today and in the future. By adapting, adjusting and learning through successes and failures, an agile culture can lead organizations to quicker — and greater — success.

What is leadership’s role in customer-centricity?

Many factors make an agile, customer-centric culture, but a key component is the leadership’s role in creating, supporting and maintaining it. Leaders set behavioral standards and enforce them through their actions, the activities they reward and the things they overlook.

For instance, mixed messages are sent if an organization claims to be customer-centric, but leaders only reward employees who cut costs at the expense of customer experience. The public message is “we love our customers,” yet leadership only supports customers when they don’t involve other business objectives like profitability or efficiency.

That said, it’s a different story when efficiency is used to benefit the customer. What leaders emphasize matters. If there is a direct line between creating profitability and efficiency and benefiting the customer, leaders can have the best of both worlds. Leadership often fails when they don’t connect the dots for their team members.

Ultimately, leadership plays a vital role in agile, customer-centric organizations because they reinforce the behaviors making up the culture. They also demonstrate to employees that actions to achieve company objectives can align with customer needs.

How does an organization get started?

Building or changing a culture isn’t going to happen overnight, but shifting toward agility and customer-centricity is possible with leadership buy-in. Leaders must start addressing these key areas.

Prioritization

It is never enough to say you are a customer-centric organization or claim your teams are nimble to adapt to internal or external changes. What gets prioritized determines the culture and behaviors in an organization. 

De-prioritizing customer-centric activities or initiatives sends a clear message that even though leadership claims to want one thing (customer-centricity), those efforts take a back seat to other priorities when push comes to shove.

Resistance to change

As much as your culture may embrace agility, humans naturally resist continual change. Some things make it more manageable, such as: 

• Having a reason and purpose behind the change so it’s easier to understand.
• Involving your team in planning the change. This increases the likelihood of buy-in, participation and championing to others within the organization.

Consistent messaging

Just as you send on-brand messages to customers, cultural change within an organization requires consistent communication. Don’t just assume employees only need to hear something once to get on board or understand its meaning instantly.

Engage in consistent communication campaigns internally. This builds an environment and culture with an agile, customer-centric focus enough to propel your brand as a category leader.

Additionally, start by building an appreciation for experimentation and learning. Remember, not every experiment needs to yield positive results to teach valuable lessons. Leadership must support their teams’ growth, even without achieving desired outcomes 100% of the time. 

Set up your organization for long-term success

As you’ve undoubtedly seen in your career, good ideas are not enough. It takes a great team to do excellent work and continuously improve in a dynamic and competitive environment. 

Building an agile, customer-centric culture sets your brand up for success. When you continually focus on customers and create a motivating and rewarding work environment, your organization will stand out.

The post North Star goals for category leaders: Agile, customer-centric culture appeared first on MarTech.

Sales and marketing professionals understand the continual pressure to reach and convert new customers to a product or service and how easier it is to keep a happy customer than win new ones. 

It seems that brands are starting to take this to heart in a big way, so much so that according to Reuters, “lifetime value” is Silicon Valley’s next buzzword. Today, the marketing technology industry focuses on providing solutions for leading brands to measure and capture greater customer value over the long term.

In this third article in a four-part series, I will discuss why brands must adopt a customer lifetime value model as one of their primary organizational KPIs and the importance of creating long-term, loyal customers over continual customer churn from dissatisfied customers.

What is a customer lifetime value model and how is it calculated?

Let’s begin by making sure we have a good understanding of what exactly a customer lifetime value (CLV) model is.

While the name is descriptive, what we are doing is understanding the total potential value that an individual customer can bring to our organization once we acquire them.

This goes well beyond an initial sale and many organizations invest quite a bit in acquiring a customer — in some cases losing money on an initial acquisition — to generate a lot of value over the long term.

There are quite a few different methods to calculate CLV, but a (relatively) straightforward method is the formula below:

For each of those terms in the equation above, here’s a definition:

• Purchase frequency (PF): How often the average customer buys your product or service. Choose a measurement for frequency that makes sense for your business. For instance, a car manufacturer and a quick service restaurant will have different time frequencies that make sense. The former might be in years and the latter in weeks.
• Average order value (AOV): The average amount a customer spends with your brand when they make a purchase based on the total value of purchases by new and existing customers.
• Gross margin (GM): This helps you calculate your profit on each order and gets to a much more accurate number than simply using the average order value (AOV) to measure how much you make from the average customer. Gross margin is calculated as the total sales revenue minus the cost of goods sold (COGS) divided by total sales revenue.
• Customer lifespan (CL): This is the average amount of time a customer continues buying your products and services. Again, make this in the same unit of measurement as your purchase frequency (weeks, months, years).
• Number of new customers: This is the number of new customers you gain within the same unit of frequency chosen for purchase frequency and customer lifespan.

Beyond the mere calculation of CLV, an organization must embrace the total value a customer can bring as a factor in strategic planning, culture and key performance indicators (KPIs) that drive decisions.

Dig deeper: The one martech metric that really matters: Customer lifetime value

How does a CLV model change a company’s goals?

You’d be hard-pressed to find a successful organization that doesn’t value a long-term customer. But there is a big difference between simply wanting to create a great customer experience and actually delivering on it so that your customers buy more often and refer others to the brand.

To embrace a customer lifetime value model is to shift the strategy and direction in several key areas. Let’s discuss three of these, though I’ll admit there can be many other benefits in addition to the ones below.

Organizational KPIs become aligned with customer success

The first thing that adopting a customer lifetime value model as a strategic KPI changes about your company’s goals is that it makes it very clear that customer success equals business success. 

While short-term sales and revenue goals will always be important, when CLV is recognized and adopted as a primary goal, teams and initiatives prioritizing long-term customer success gain more latitude to treat customers well to build loyalty and grow their value over time.

Acquisition and retention goals gain greater alignment

If there is any friction between sales, marketing and customer service or support within your organization, you are likely experiencing a conflict between the need to acquire new customers and the need to retain them. 

When a customer lifetime value model is embraced as a primary KPI, it becomes necessary that the quality of leads be such that new customers become lifetime ones. 

While this may already be a goal of all teams, it is easy to compromise to get some net new customers simply “in the door” to hit a sales quota or marketing target. 

When customers who are not good fits for your products and services are no longer prioritized, you can focus more on high-quality potential lifetime customers.

Attribution models become more holistic and multi-touch

The last example I’ll provide here has to do with how you measure the effectiveness of your marketing. 

If you’re accounting attribution for only new customer acquisition, you will only look at a subset of the channels that both new and existing customers are exposed to. 

When looking at a customer lifetime value model, you aren’t just looking at attribution models for which channels contribute to an initial sale. You will now look at:

• What contributes to building a good foundation initially.
• What channels contribute to keeping that customer engaged and loyal. 

In the best scenario, this also means that you would be switching from a first- or last-touch attribution model — giving the “win” for a conversion to either the first or last channel that an audience member saw or interacted with — to a multi-touch attribution model, that can give “credit” to all of the channels that a customer interacted with throughout their journey. 

Doing this is no small feat, particularly for large and complex marketing programs, but can be extremely beneficial in helping to maximize ad spending and understanding what points of interaction your customers find most valuable so they can be prioritized. 

And, of course, not all of your customers are exactly the same either. Some will find specific channels more valuable and prefer them over others so this is not a one size fits all approach, either.

Focusing on the long-term value that you provide to your customers and what your customers provide to your business can reap many benefits, though it does require a commitment from many parts of the organization to hold true to this as a principle. 

Dig deeper: Marketing attribution: What it is, and how it identifies vital customer touchpoints

Making CLV happen

So, while all of this sounds great, you might be thinking to yourself that this would be next to impossible in your own organization for one of many reasons. These could include:

• Silos that prevent data from being tracked and followed.
• Teams that don’t share acquisition or sales data with one another.
  A lack of a common taxonomy to reconcile activities across departments, product lines, or divisions. 
• Or any myriad of other issues. 

To this, I say that doing this all at once may be difficult, but that doesn’t make it a worthwhile endeavor. As with most important initiatives, you need to start somewhere, so figure out what you have and build from there. An iterative approach with a minimum viable product (MVP) approach in mind can go a long way. 

In the case of a customer lifetime value model, an MVP may be a lifetime view of the customer but with slightly less accuracy or fidelity, which can grow over time. Alternatively, it could be that you build out CLV across a subset of overlapping products and services. 

If you are truly starting at square one, you may need to ensure that the individual building blocks of the CLV calculation can be built and measured one by one. The important thing is to start somewhere!

In the next article, I will discuss the fourth and last North Star goal, which sets the foundation for all the work done in an organization. This is the culture of the organization, wherein the goal is to be agile and customer-centric in all things. We’ll see why this is important and why this is good for both customers and employees.

The post North Star goals for category leaders: Customer lifetime value model appeared first on MarTech.

Today’s customers have two, sometimes opposing needs. They want to receive highly personalized content, offers and experiences while maintaining a high level of data privacy. Enhancing the customer experience and acting as good stewards of customers’ private information is a win-win for brands.

My latest book, “House of the Customer,” outlines four North Star goals that every brand should strive for, no matter how aspirational they may seem. In the previous article in this series, I discussed the first of these goals — providing a truly one-to-one, omnichannel personalized customer experience. 

In this second article in a four-part series, I will discuss having a truly first-party customer view that protects customers’ data while allowing the brand to incorporate accurate insights to provide great experiences.

Why is first-party data so important?

For those less familiar with this topic, you might be wondering what the fuss is all about. After all, you have a CRM in place and obtained permissions and opt-ins for your email marketing. So, let’s discuss a few reasons why first-party data is so important. 

The industry is changing

I’ll discuss government regulation next, but the marketing technology industry itself is making big changes — in some cases amounting to self-regulation. Some of these changes include:

• The deprecation of third-party cookies — with Apple and Microsoft taking the first steps and Google to follow in the months ahead.
• Less invasive mobile device tracking. 

Additionally, the traditional use of third-party cookies to target advertisements is getting disrupted by privacy-focused data clean rooms, which trusted parties only share.

With this approach, publishers build their own advertising networks (or partner to build value in their networks) and other solutions. This gives marketers and advertisers greater confidence in their ability to reach their audiences in a way that is mindful of consumers’ growing desire for data privacy. 

Dig deeper: Why we care about data clean rooms

Regulatory and compliance needs

More changes are coming in the industry, some of which are driven by regulations. The world is shifting toward greater transparency and oversight of consumer data privacy.

The European Union’s GDPR leads the way, followed by others in California, other states in the United States and other countries worldwide. 

This shift requires that companies of all types and sizes:

• Secure customer data. 
• Understand how their customer data is sourced and used.
• Responsibly utilize it to provide personalized experiences to stay competitive. 

Dig deeper: Why we care about compliance in marketing

Personalization 

First-party data is important because better, more relevant and accurate data are needed to provide highly personalized experiences. 

By now, you have surely read the statistics on how greater relevance helps create more customers, loyalty and word-of-mouth referrals. Even though some claims on personalization driving revenue are overstated, plenty of evidence supports the claim. 

Clearly, the time for brands to adopt first-party data strategies is now. While the above reasons are all compelling, so is the fact that your competitors are likely focusing and investing in these areas as well. This is even more reason to move quickly. 

Dig deeper: What is personalized marketing and how is it used today?

What are the components of a first-party data strategy?

Now that we understand the importance of a first-party data strategy, let’s talk about what goes into creating one. To do this, we will explore the three major components. 

1. Unified customer views

The first component involves creating a single view of the customer across all channels and platforms where you might be storing information about them. 

Tying marketing, advertising, CRM, customer service and other data together into a single cohesive view provides us visibility across our business and the customers’ journey before, during and after the sale. 

2. Consolidated tools

The second component to discuss is sometimes a natural byproduct of the first. A need to unify the view of the customer brings with it a need to consolidate the tools used to collect, manage and analyze that data. 

With a customer data platform (CDP) at the core, seamlessly integrating disparate systems or removing overlapping platforms provides a sustainable way to keep a robust first-party view of customers. This also helps you to take actions based on this view, using tools like customer journey orchestration and a next-best-action approach. 

3. Data governance

The last component of a first-party data strategy is customer data governance. Fragmented data poses a risk, and inaccurate or incomplete data causes customer dissatisfaction. 

How you collect, manage and update customer data greatly impacts your customers’ trust in your brand. 

Data governance isn’t just a one-time initiative, either. It requires consistent maintenance and training of the teams entrusted with valuable customer data. Thus, guidelines and how customer data is utilized must be regularly reviewed and updated to ensure best practices and compliance with regulatory requirements.

While these three components may contain a lot of individual pieces, they provide an overview of the scope of your first-party data strategy. 

How does this change your marketing approach?

Although you may have some or all of the above elements in place, an excellent first-party data strategy is about more than just the right pieces. Its effectiveness lies in how you use those pieces. Let’s explore a few ways it may change how you perform some of your marketing. 

It’s time to plant your brand garden

This may not be a big change for companies cultivating their first-party data for years.

But if you have an intermediary selling your products or if you have been heavily reliant on third-party data for advertising, start building a robust infrastructure to communicate and sell directly to your customers. Or, as some refer to it, a brand garden.

Don’t just ask more questions — ask the right ones

It could be easy to interpret the need for more first-party data to mean you need to start asking customers many more questions. But that isn’t always the case. Customers want to ensure they share information that feels relevant to your brand and will provide you the ability to tailor their products and experience better. 

If you ask many questions that don’t seem to have anything to do with the products or services you offer, you risk losing your customers’ trust. So make sure to keep your requests for data relevant and clearly demonstrate the reward customers get for sharing more. 

Consider a cooperative approach

Is a brand garden not right for your business? Don’t have enough opportunities to ask customers questions directly?

Then consider finding complementary brands and use a cooperative approach of sharing first-party data to broaden your ability to reach customers and personalize your offers, messaging and experiences. 

This approach can include shared customer data clean rooms and other joint efforts. Just make it absolutely clear to your customers what you are doing, who you are partnering with and why it benefits them. After all, customers are already wary of their data being shared by unknown parties. 

Even if you are mindful of only working with parties your company trusts, ensure your customers understand your process of vetting trusted partners.

While aspects of your marketing may remain unchanged, having a greater focus on collecting, utilizing and safeguarding your customers’ data might change some parts of your marketing but will improve and safeguard your brand and its future efforts. 

How do I get started?

Even though dates such as Google’s third-party cookie deprecation still seem in flux, it doesn’t mean starting immediately isn’t critical. Here are a few ideas if you haven’t started on this yet.

Determine the current state of your first-party data

You can start by better understanding where your organization is on a journey to greater customer data maturity. You may be further along than you think, even if there may be many dots to connect to have a true first-party data strategy.

Evaluate your third-party usage

Next, you should better understand where you rely on third-party data and what impacts there will be as the industry evolves. Create a gap analysis so you can start planning immediately.

Create a first-party data strategy to fill the gaps

Once you have the first two steps in place, you can then build a strategy and implementation plan to:

• Shore up your first-party customer data gaps.
• Compensate for any shortcomings your marketing infrastructure will have if you minimize or discontinue the use of third-party data.
• Build for the future personalized customer experiences your audiences crave.

Strike the right balance in your first-party data strategy

As you can see, having an effective first-party customer data strategy means that you can balance two sets of customer needs.

• First, you will have the capabilities to support a more personalized experience.
• Second, you will have greater control over your customer data to maintain the highest levels of privacy.

In the third article in this four-part series, I will discuss the need for brands to embrace a customer lifetime value model and the benefits it can bring both the customer and the business.

The post North Star goals for category leaders: First-party customer view appeared first on MarTech.

As marketers, we must constantly balance the ideal with the practical and achievable. This comes amid a flood of competing priorities, including: 

• Demands for short-term financial results from shareholders.
• Leaders needing to achieve business objectives.
• Customers demanding the best possible experience.
• The competition catching up or pulling ahead. 
• All the chatter from our own community about the latest and greatest technology and how quickly every brand has to adopt the newest three-letter marketing acronym. 

Because of all this, it is easy to lose sight of the long-term goals that will help us achieve continued success and build loyal, lifetime customers.

I use the term “North Star” goals to mean those ideal points we should be navigating toward, even if they feel unreachable at the moment. Just like the North Star guided travelers throughout history, these marketing goals will guide today’s marketing leaders toward the optimal way of providing the customer experience of the future. 

As an advisor and consultant to marketing leaders at top brands, I have seen four overall trends that set category leaders apart from their peers. I took these four trends and turned them into a set of North Star goals that I also explore further in my latest book, “House of the Customer.“

This four-part article series will explore each of the trends and goals in depth, including what they are, how realistic the goals are for most brands and the first steps you can take to achieve them.

In this first article in the series, I will explore the first North Star goal — providing a one-to-one, omnichannel personalized experience.

Let’s explore this by breaking down each of the three parts of this goal and then we’ll tie it all together and explore just how realistic this is for most brands today.

Breaking it down

To start this discussion of one-to-one, omnichannel personalization, let’s make sure we have a good understanding and example of each component.

One-to-one personalization

The first part of our goal means thinking beyond broader audience segments and treating each customer individually. While they may share behaviors and traits with other customers, what we do for them factors in. 

For instance, if you are doing personalization broadly, it is based on some type of segmentation, like sending an email to all customers who bought a product within the last 10 days. While tailored to the recipients, the message doesn’t consider any specifics beyond the date of purchase.

Simply saying “Hello Greg” in an email to me is what I refer to as a simple substitution, one slight step above saying “Hello Customer.” 

Sending a truly personalized email message means using the information you already have about me, such as the type of product or service I recently purchased or reviewed. 

If I’m a frequent customer and you haven’t heard from me lately, send a reminder that it’s time for me to buy again. Or if I rarely ever engage over email but am responsive via SMS, take that into account. 

In other words, give me something beyond a generic message that could apply to anyone based on what you know about me and what I might want to see.

Omnichannel engagement

Brands that can deliver meaningful personalized content, offers or experiences on a channel like a website, mobile app or even in an in-person environment are likely to make a great impression, if not a sale. 

But today’s consumers don’t use a single channel or method when researching, purchasing and using our products or services. Thus, brands must ideally provide seamless and consistent experiences across all channels — or omnichannel.

Doing omnichannel well involves consistent messaging and visuals across channels. More importantly, customer data is carried across and utilized to tailor content, offers and experiences in both digital and offline worlds.

Dig deeper: The ROI of personalized experiences: Audience measurements

How realistic is this for most marketers?

You might think all this sounds great, but how realistic is it to implement an ideal one-to-one, omnichannel personalized experience for customers?

Anyone working on an effort like this can attest that it is still a lofty goal, even for many sophisticated organizations. Let’s discuss hurdles that often get in the way of achieving this.

Data and platform silos

Let’s start with the technical part first. Access to customer data, access to platforms or even one platform’s access to another are common challenges for large and small organizations.

Such issues prevent us from connecting the dots both for ourselves as marketers and for customers and the experiences they can receive.

Dig deeper: How to overcome data silos and fragmentation

Organizational silos

Similar enough to the data and platform silos, bureaucracy or lack of communication and planning channels may get in the way as well.

For instance, how are you supposed to create an omnichannel experience when the website team, the email team and the advertising team aren’t able to coordinate their efforts?

Cost versus benefit

Finally, when looking at how realistic this might be, you need to consider costs versus benefits.

While we’ve read statistics on how consumers are more likely to buy based on personalized experiences, you must determine how to incrementally move toward this one-to-one omnichannel approach while making smart investments over time. 

After all, there might be major benefits from specific improvements, while others may have no effect. Taking a test-and-learn, iterative approach can benefit your business greatly here.

How do I get started?

While we just went through a few reasons why this goal may not be immediately achievable, rest assured that there is hope.

The good news is that while “one-to-one” and “omnichannel” may be the goals, you can start incrementally and work your way toward them. Let’s discuss a few ways to do this.

Start with segments instead of one-to-one

One-to-one personalization is our goal, but we often need to start somewhere. This means that we can start with audience segmentation. 

You’re almost certainly segmenting your users in some ways already. So rather than focusing on broad categories, create additional types of segments or more detailed ones based on behavioral and other data-driven factors. Getting more granular with your segments can be a good bridge to a one-to-one personalization approach.

Build from a single channel to multi-channel

While omnichannel sounds great, it’s not easy even for organizations with more resources. Large organizations have more extensive requirements due to working in multiple countries and languages, and often with more third parties such as platforms and distribution partners.

Because of this, the best approach is to build from single, disconnected channels to a multi-channel approach. Add a channel at a time to a seamless experience and allow your brand the ability to focus on optimizing one channel at a time. 

Avoid trying to tackle everything at once, which might indefinitely delay your efforts while trying to solve the data, platform and organizational hurdles discussed earlier.

Dig deeper: How brands can create omnichannel customer experiences

Build bridges with your partner teams

Just like connecting the dots between data and platforms, building connections with the other teams in your organization is vital. You will need allies to move toward a one-to-one, omnichannel personalization approach. 

If you are in marketing, make sure you know who your key allies on data and technology teams are. Have conversations about what your North Star goal is and involve them in the solution from the very beginning.

Start with a proof of concept

Finally, you may be in a situation where you can’t simply convert all audiences, or even the entirety of a single channel to use a personalized approach. 

If your brand has multiple, distinct audiences and many product categories and lines, it may make more sense to use an iterative approach where you start with an audience segment or product type. 

For instance: 

• If your company sells both consumer and business products, each with various different needs, you might decide to start with your consumer audience.
• If you sell both computers and headphones, you might start with your audience for headphones. 

This doesn’t mean that you won’t get to all of them eventually, 

Start with a proof of concept such as, “We will personalize our experience for consumer headphone customers across three channels.” Doing so allows you to get the data and learn about internal difficulties in setting up the initiative, all while achieving a focused goal in a reasonable amount of time and money.

The path to one-to-one, omnichannel personalization

One-to-one omnichannel personalization may not be attainable for your organization immediately. But by building a consistent experience one channel, platform and audience at a time, you can work towards that goal and make consistent progress doing so.

In the next article in this four-part series, I will discuss the importance of a first-party data strategy, our second North Star goal.

The post North Star goals for category leaders: One-to-one, omnichannel personalization appeared first on MarTech.

After examining how audiences and content are measured in terms of personalized experiences, let’s discuss how brands should approach the process that drives personalization. 

Process measurements require looking at the methods used to personalize, how efficiently they are performed and how they are improved over time.

In this article, we will:

• Cover three aspects of operationalizing personalization.
• Do a reality check for those brands that want to go all in on 1:1 omnichannel experiences.
• Explore the viability of doing this and the cost of not doing it.

Great personalized experiences require alignment across teams

Organizations that are misaligned and have disconnected internal operations will have difficulty providing seamlessly personalized experiences externally to their customers. Let’s look at a few ways this plays out in the real world:

• Siloed marketing teams, where the “ecommerce team” doesn’t regularly interface with the “email marketing team.”
• Siloed departments, where marketing, data and engineering all might as well exist on different planets.
• Siloed product teams where widget A is marketed and supported in a completely different manner than widget B.

To make it even more challenging, some organizations have all of the above. This doesn’t mean you can’t start with some low-hanging fruit. Focus on building bridges where there are the most commonalities and potential benefits.

For instance, if the processes to create mobile app content and email campaigns often overlap, start there. Sure, it won’t provide omnichannel personalization overnight, but you can build consistency and, more importantly, a case for why more coordination and collaboration are needed within the organization.

Breaking down silos and having greater coordination inside your organization is a key step toward creating more holistic and valuable personalized customer experiences.

Dig deeper: Managing the unpredictable: Getting marketing, sales and operations aligned

Testing against hypotheses eliminates anecdotal noise

Most marketers have an opinion about how effective personalized experiences are in influencing engagement and conversions. The challenge is that many of these opinions are anecdotal and what I would call less than scientific. 

To counter this, we need to run true tests, which include:

• A hypothesis (what our assumption is).
• A null hypothesis (what must be rejected first to determine that the hypothesis can be true).
• A threshold of statistical significance that justifies further testing and/or investment in the efforts. 

In other words, welcome back to Statistics 101. 

The best way to determine the effectiveness of your personalization in this way is to do a true A/B test, where the “A” variant provides all users with a generic message/offer/experience and the “B” variant personalizes it. With statistically significant data, you will be able to see if your efforts to personalize

I also recommend that you examine this in a few dimensions. Personalization can be more subtle or more extreme. The cost to deliver — whether that is actual hard costs or time and resources — can vary depending on how extensive that component needs to be personalized. For instance, creating an endless variety of customer imagery can be resource-intensive, while doing database lookups can have a minimal cost once the initial rules are set.

Regardless of how extensively you approach personalization, creating a culture of testing and validation ensures you focus on the right things, cutting through the clutter and anecdotal noise that holds teams back from greater success.

Dig deeper: Why testing is a marketer’s most powerful tool

Feedback loops and continuous improvement

Of course, even rigorous testing is only as good as the process used to incorporate the findings of those tests back into the workstream. This requires a commitment to consistently find ways to enhance and optimize personalization efforts. Two big pieces are a feedback loop and governance over the process.

First, you must create a feedback loop that takes your learnings from your efforts (including your tests) and ensures the people and platforms that rely on them are connected. 

I’ve worked with organizations that were great at measuring and creating in-depth reports of exactly what happened, where and to whom — but had no meaningful way to translate those results into any changes or actions for the next time they needed to do something. 

They had a beautiful library of charts and reports. Yet, their efforts never improved, other than by anecdotal sharing of what made it into reports and what must have been lucky guesses.

Additionally, you need a set of processes to ensure you can change and adapt by incorporating feedback while also not changing too much too quickly. This prevents internal teams — and your customers — from getting confused or frustrated by too much of a well-intentioned thing.

This is where a governance model for your personalized customer experiences will play a role. Remember, it’s not always about moving quickly. Instead, a good governance model:

• Has transparency and consistency.
• Moves at the right speed to allow you to adjust your personalization efforts.
• Avoids too much change that might overwhelm your teams or provide inconsistency to a customer’s experience.

Feedback loops and governance models standardize and systematize your ability to continuously improve the customer experience and, consequently, the ROI that personalization efforts can deliver.

Dig deeper: Implementing agile marketing experiments leads to leadership buy-in

Can lagging organizations catch up to the leaders? 

Some of you may be reading this and thinking that all of this sounds amazing, yet it’s simply not possible in a short timeframe. The leaders in personalized experiences aren’t pausing for the laggards to keep up. 

Large brands may struggle with departmental or product silos. Smaller ones may struggle with the resources and infrastructure required to do all of this well. Setting up the systems and platforms that support personalized customer experience takes investments.

The hard truth is that, despite the challenges, it is imperative for companies that have fallen behind to catch up. Each day that goes by, the gap between the laggards and the leaders continues to expand. The processes, platforms and knowledge from testing — and even missteps — that the leaders gained will only grow more valuable.

In other words, choosing whether to offer more personalization or not isn’t what you should be considering. Instead, it is how you will bridge the gap between you and the competition, all while maintaining profitability and not disrupting either internal (employee teams) or external (customers and partners) audiences.

An iterative, incremental approach is the best and really the only way to do this. A strong prioritization model can help you understand which initiatives will have the biggest impact on the business and your customers while having the most minimal impact on resources.

Measuring the ROI of personalization

Getting a true return on investment from creating and delivering personalized customer experiences requires a holistic view across audiences, content and channels and the processes used to create, manage and continuously improve all of the above.

The post The ROI of personalized experiences: Process measurements appeared first on MarTech.

While doing personalization well poses challenges to even the most sophisticated brands, offering personalized customer experiences is increasingly becoming a distinguishing factor in high-performing companies. 

Companies excelling at personalization can generate up to 40% more revenue than those deemed average at it, according to McKinsey.

In the first article in this series, we looked at measuring the performance of personalized experience by how customers reacted, whether individually or within audience segments. 

The next way to measure the ROI on personalized experience is by looking at the performance of content and its contribution as part of a specific experience and across the entire buyer’s journey. 

Individual and incremental content performance

Let’s begin at the most “zoomed-in” view by looking at how individual elements perform. After all, if something isn’t working at the micro level, chances are there will be issues in the bigger picture. 

Some of the ways of looking at individual performance include:

• How an individual piece of content or an individual channel performs.
• Performance on an individual stage in the buyer’s journey or by audience segment.

To take an incremental performance approach would mean looking at things in terms of:

• Providing personalized content and experience vs. providing no personalization.
• Providing cross-channel personalization vs. personalization on a single channel (or none).
• Single channel performance utilizing personalization vs. not using personalization on that channel.

These are also important because personalization takes time, effort and other resources to accomplish. Find out if there are areas you get better results at than others so you can be as efficient as possible. 

Dig deeper: How to enable greater personalization in a world of impersonal experiences

Multi-touch attribution 

As a marketer, you’ve undoubtedly asked this question and as a customer, you’ve been asked a million times, “How did you hear about our product or service?” 

In this world of channel switching and always-on marketing campaigns, it’s most likely that a customer hears about a product or service from not one but five or six channels — though they may only likely remember one or two. That doesn’t mean, however, that each of the six channels they were reached on had no effect.

The next way to measure the performance of personalized content is to use a multi-touch attribution model, which considers all the methods a customer might be reached and determines each of those channels’ contributions to a sale.

• A single channel’s contribution to the sale.
• The optimal order of communications to create a conversion.
• Channels that have the least amount of lift (which can be removed and potentially save marketing dollars).
• The optimal first- or last-touch channels that create the biggest impact.

While John Wanamaker famously said that half of his advertising didn’t work, but he just didn’t know which half, the true answer may be much more nuanced than that. 

Using a multi-touch attribution model, Wanamaker may have found that 75% of his advertising contributed to some type of lift, regardless of how small each individual contribution was.

Dig deeper: Marketing attribution: What it is, and how it identifies vital customer touchpoints

The cost of personalization

Brands should also not take for granted that personalization is solely a value-add endeavor. There is a cost to:

• Creating content variations.
• Planning multiple variations of automations and journeys.
• Measuring those efforts.
• And all of the other activities. 

This doesn’t mean that providing personalized customer experiences isn’t worth it. That said, taking a realistic and pragmatic approach from the start will serve you well throughout.

Here are a few things to consider:

• Resources need to create the content variations (text, imagery, videos and more) that personalization requires.
• Resources to manage multiple variations of campaigns, offers and experiences.
• What happens when details change? You will need a taxonomy and categorization system to streamline this process.
• Platform costs to support personalization, measurement and analysis.

I don’t mention these things to dissuade you from making further investments in personalization. Instead, it is important to proceed with a realistic mindset. 

Also, to do this well often means to approach it incrementally so that the investments in process and platforms are incorporated more easily by your people.

Dig deeper: How marketers can prioritize digital experiences

Beyond performance, how do we measure if personalization is worth it?

So far, we’ve talked about how to do it, but the question still remains — is it always worth it to invest in creating omnichannel personalized experiences? 

After all, 63% of marketers said last year that they struggle with personalization, according to Gartner. 

Additionally, because the technology and data requirements of multi-channel personalization also require larger-scale changes, it probably doesn’t help to see that 84% of digital transformation initiatives are said to fail as well.

So how do we reconcile the fact that there is so much potential benefit to personalization, yet the costs and risks associated with it are also very real? 

The most successful approaches are those that are done incrementally, using lean and agile methods such as minimum viable products and systems of continuous improvement.

Make no mistake, personalized experiences are the future of marketing and customer experience. How quickly brands get there can also make the difference between who becomes and remains a category leader.

The post The ROI of personalized experiences: Content measurements appeared first on MarTech.

Recent statistics support the need for brands to create more personalized customer experiences.

• 80% of consumers are more likely to purchase from brands that provide tailored experiences.
• 70% of consumers say their loyalty is impacted by how well a brand understands their individual needs. 
• 71% of customers get frustrated when they don’t have personalized experiences. 

While all of the above may be true, it can be hard to determine the return on the investments needed to create truly one-to-one experiences. The path to doing so can take people many months, plus millions of dollars, to get right. 

In this three-part series, I will explore how marketers can measure performance and returns when creating personalized customer experiences. I will also cover several questions that any organization should ask before embarking on what could potentially be a large-scale initiative.  

Let’s start our discussion of measuring personalized customer experiences by focusing on the important aspect — the customer. Measuring personalization by audience performance means that we are measuring both individuals and groups of individuals and how they react to more tailored content, offers and journeys. 

In this article, I will go over different ways of looking at audiences and personalization and address some of the skepticism around the returns on one-to-one personalization’s efficacy. 

Understanding the audience is key to personalization

We’ve all gotten emails or text messages that say the equivalent of, “Hey [insert your name here], would you like 50% off on [insert product or service here]?” 

Some may call that “personalization” because your name was inserted instead of simply saying, “Hey, random person.” But this is not the type of personalized experience I want to discuss here. 

Let’s call the approach I just described “substitution” rather than “personalization” and focus on more robust examples and ideas instead. 

To do anything beyond simple substitution, however, we need to understand much more about our customers or potential customers than just their first and last names, email addresses or phone numbers. 

Enter the first-party data strategy and the reason you see so many brands investing in tools like customer data platforms (CDPs) and even second-party platforms that pool customer data among trusted parties. This strategy is crucial now that third-party cookies and mobile device ID tracking are being deprecated by major technology companies. 

So here’s a question, if personalization depends on understanding your customers well, how do you know how well you understand them?

The answer lies in creating a first-party data strategy and infrastructure that allows you to build customer profiles and evolve their information over time. 

Building a first-party data strategy requires: 

• The right infrastructure (e.g., CDPs and CRMs). 
• The trust of customers who provide their data to you in the first place.
• A way to continually enrich and serve customers with personalized experiences based on that data.

Dig deeper: What is personalized marketing and how is it used today?

Relative measurements are critical to understanding personalization lift

This one is for the true personalization skeptics out there. How can you understand if your personalization is working unless you set up a true experiment and measure the difference between using personalization and not? Not very well, indeed!

This is why a relative measurement can provide many insights about how your personalization is working (or not). Think of it in terms of the following (which you’ve probably seen before):

• Show a customer the exact product they bought in imagery on the website or an email.
• Show a customer the product they customized on their last visit.
• Customize imagery the customer is shown based on their geography or other demographics.

Then, compare the results of doing that by showing them a generic product or other one-size-fits-all text or imagery. You may see a lift in some areas and not others, but this is part of the value of using relative measures.

Because creating personalized content, offers and experiences takes more resources than a one-size-fits-all approach, it’s important to have a better understanding of the levers that generate the most value. 

Eventually, you may be able to personalize everything for everyone on every channel. But in the meantime, knowing what aspects have the biggest impact can help you make meaningful improvements without the resource drain. 

Dig deeper: How to humanize the digital experience with first-party data

CLV is the ultimate measure

While there are many helpful measurements to determine the effectiveness of personalized experiences, the most beneficial can also be the most challenging to use. Customer lifetime value (CLV) requires both a wealth of information about an individual’s full set of actions and, perhaps, the most precious commodity of all — time. 

Measuring CLV enables us to truly see the effects a comprehensive, personalized customer experience can have on the buying and product or service usage of an individual. It factors in the cost to acquire a customer, which can often be an investment to convert them and then demonstrates how a single customer can drive value over time.

Of course, the time-based component does make this the most challenging. For instance, if the average lifespan of a customer is over five years:

• How do you get a useful CLV model in a relatively short amount of time?
• How can you tell what role personalization plays in that model?

I’ve seen many different models for calculating customer lifetime value, but they will use historical data to create averages for spend over lifespan, churn and more. You can use these as your baseline measurements.

In addition, you can use relative measurements to see the effect on customer lifetime value for those customers that received less personalized experiences versus newer customers that might have benefited from more personalized ones.

I’ll talk more about multi-touch attribution in a later article in this series, but being able to attribute value and conversions to specific interactions and channels or touchpoints can also help when you are asked a question about determining the value of personalization in the overall CLV.

How much personalization is enough?

Beyond the statistics telling marketers personalization has a positive impact on buying behavior, as consumers ourselves, we appreciate it when our experiences are tailored by brands. 

But that personalization comes at both a cost to the brand (which might be passed on to us as customers) and potentially to the amount of data we give away (which can affect our data privacy). So the question remains, how much personalization is enough? And is there such a thing as too much personalization?

There are a few ways to look at this. 

• From an internal resource perspective, too much personalization too quickly can drain resources if the right systems and processes aren’t set up to handle the increased needs. 
• True one-to-one personalization relies on artificial intelligence and machine learning (AIML) models and predictive analytics that can work very effectively but needs time and training to do so. 

So perhaps instead of asking how much personalization is enough, it would be better to ask: 

• How much personalization is enough to create improvements now?
• What should we be building for the future?

Taking this approach means that your customers can benefit from a more tailored experience while your internal teams and infrastructure adapt to the changes needed to continue making these personalized experiences more effective.

When you pay attention to these important aspects of your audiences, measuring the ROI on personalized content, offers and experiences becomes greatly valuable.

The post The ROI of personalized experiences: Audience measurements appeared first on MarTech.

Customer journey orchestration (CJO) is supported by many platforms in addition to a single CJO application. Orchestration requires that omnichannel content management, customer data, testing and personalization, as well as analytics and reporting platforms are aligned. Therefore, we need to keep all of these in mind as we plan for CJO implementation.

In the last article in this three-part series, we are going to explore this by looking at four critical platforms necessary for your organization’s success with customer journey orchestration.

Customer data

Let’s start by looking at the platforms that hold your customer data. Understanding your customers and anticipating their needs is a key component of customer journey orchestration, so access to timely and accurate data about your customers is critical.

As you are planning for CJO, here are some things to consider related to your customer data:

• Do you have a customer data platform (CDP) already, and is it your primary “source of truth” about customers, or do you have other platforms like a CRM?
• How is customer data being stored and accessed?
• How are you anticipating a move to a first-party data strategy?
• How will you use audience segmentation in CJO?

To help with these issues related to customer data, you should get started on a first-party data strategy (if you haven’t already). This means that if you are heavily reliant on third-party data for advertising and other marketing, you need to shift to collecting more customer data that you have permissions to use, and the ability to deploy as you begin orchestration.

Begin customer journey orchestration with some low-hanging fruit. Work with the teams dealing with customer data to define target audience segments and build a good proof of concept.

Ensuring your customer data systems are accessible, accurate and up-to-date sets up your CJO implementation for success.

Dig deeper: How clean, organized and actionable is your data?

Content management

Your customer journey orchestration platform is going to need access to all of the content for the channels you wish to orchestrate. Additionally, the personalization opportunities that CJO allows mean content variations may include localization, testing variants and other pieces of content that will be tailored to customers throughout their journey.

Some key considerations around content management include understanding:

• How you will add or modify channels over time as new content is needed.
• How you will categorize content across channels to understand relationships between similar content on different channels. 

This means that you should have a way to understand that email content is related to website content, or mobile app content, since your journey orchestration may target different audiences on different channels, but need similar content.

To get started, you should do an audit of your content management system (CMS) landscape. Depending on your organization, you may have several different systems serving different properties. Get an understanding of whether you can consolidate any of them. Having a single system can streamline the processes needed to create content as well.

Consider using a headless CMS, which is built to serve content on a multitude of channels (not just websites).

CJO can complicate the way content is managed and organized. It is crucial to carefully plan how you will organize your content management systems to streamline your efforts as much as possible.

Dig deeper: What are headless or hybrid content management systems?

Customer journey orchestration platforms

We certainly couldn’t have a series of articles about customer journey orchestration without discussing CJO platforms themselves. As you might imagine, your customer journey orchestration tool will play a central role in your efforts.

There are many considerations with CJO. Will you go with an all-in-one platform that handles many things including customer journey orchestration? Or will you choose a best-of-breed product? You could also build components of CJO with an internal engineering team if you have the resources.

Additionally, consider how strong a role you want artificial intelligence to play in your CJO. Some systems allow very strict orchestration driven by you and your team. Others put more control in the hands of AI and machine learning to use a next-best action approach. While both have their merits, you must understand the distinction and make sure the one you pick is the right fit for your organization.

A good starting point is a proof of concept (POC) or minimum viable product (MVP) which allows you and your team to get a feel for a system without a big investment of time or money. This is arguably the most critical piece of your CJO approach.

Analytics and reporting

Last, but not least, let’s talk about measurement. The first iteration of your customer journey orchestration implementation will ultimately need improvement. What should improve and how, though? This is where your analytics and reporting platforms come into play.

Consider the key performance indicators (KPIs) and measures of success of your customer journey orchestration efforts. In other words, how will you determine that CJO is more successful than previous efforts? 

Also, consider that since orchestration involves multiple channels in most cases, having clear attribution of how channels are performing and contributing to a conversion is critical. This multi-touch attribution will help sustain the long-term effectiveness of your CJO efforts.

To get started, ensure you have a good feedback loop that helps you understand where things are — or aren’t — performing up to expectations, and what you can do to improve them.

The continuous improvement of your customer journeys and their ability to improve customer acquisition, engagement, and retention depend on the most meaningful metrics being available in useful reports. 

Dig deeper: 5 steps to make the most of your reporting and analytics

In conclusion

As we’ve seen, successful customer journey orchestration takes the three keys of people, process and platforms to be successful. There are many considerations to take into account, though it is important to always make sure you are putting your customers front and center. After all, a successful CJO means that customers are getting what they need, when, where, and how they want it.

I hope you’ve enjoyed this three-part series on preparing for customer journey orchestration. I wish you the best as you begin your own journey.

The post 4 critical platforms to support customer journey orchestration: Getting started on CJO appeared first on MarTech.