A real emphasis has been placed on the collection of first-party data.

Pairing a first-party data strategy with a one-to-one marketing strategy will help your teams remain aligned to ensure messages are both targeted and personalized. This needs to happen from the very first interaction at the point of ingestion.

Join data experts as they share their perspectives on the importance of clean, complete, and connected data to optimize your CRM.

Register and attend “How to Leverage First-Party Data for a Winning CRM Strategy,” presented by AtData.

Click here to view more MarTech webinars.

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The custom part of the proposal is a key feature. Marketing must graduate from “embedded, out-of-the-box” solutions, Frank says. He quotes Gartner research that shows that, while 55% of business leaders consider AI for every use case (rising to 71% if AI has been in use for more than four years), marketing comes seventh in the top twelve list of business functions seen by the leaders as benefitting from AI.

Why start with brand. Presenting at the Gartner Marketing Symposium, Frank made the case that brand is actually a “fuzzy, abstract” concept, and pointed correctly to the immense progress made by AI, and notably by generative AI, in handling the fuzzy. Generative AI like Chat GPT, for example, tends to sacrifice precision for broadly relevant and more-or-less accurate output. “It’s easier for them to tell you whether a story is happy or sad than whether it’s true.”

Ideal for brand, Frank says, which is not one precise concept, but a panoply of imagery, color, tone, mood and values.

“You have a brand, you care about that brand and you have been developing assets for that brand,” Frank told us. “That is actually a perfect situation to begin custom modeling.”

Of course, just starting is going to be daunting, but Frank is not calling on brands to start from scratch; “That’s out of the scope of most organizations,” he said. ChatGPT is just one of a number of foundational AI models out there, including offerings from Google and Amazon. The strategy should be to deploy one of these models and then customize it by training it on the brand’s own data. “It becomes a copy of the original model,” Frank explained, “with your own custom additions.”

As well as training data there should be human oversight and feedback, especially to represent brand values.

Are you getting the most from your stack? Take the 2023 MarTech Replacement Survey

This doesn’t mean that humans are themselves going to have to feed the model with what it needs to know. “The beauty of these models is, you don’t even have to understand the concepts that it’s extracting. It will do that for you. All you have to do is feed it with a corpus of examples and all of the subtle semantic connections that we consider it really hard to think about, it does that for you.”

Who’s on the team? This project will need input from both marketers and from IT and data scientists and AI experts. At the heart of the team, however, is a role Frank refers to as the Model Owner. The Model Owner will not be a hands-on data or AI expert, but she will be able to interact with the experts and translate between their operational challenges and the needs of the marketers. “It’s not a technical role at all,” Frank said. “It’s more of a supervisory role that articulates and owns the training process. They don’t have to know how the training process works.”

The operational framework for the model envisages generative AI creating paid media, content and social ads, sites, apps, videos and chatbots, but all within the parameters of the brand it has come to understand.

Why we care. Among all the use cases currently being described for AI, this is an ambitious one. It’s easy to see how branding could go off the rails without close human attention. Frank admits that. Also, once one starts introducing an IT team (with time on its hands) and data scientists, one begins to think this is primarily an enterprise project.

Nevertheless, Frank is bold enough to posit that custom training of AI by brands will be mainstream by 2026.

The post It’s time to teach AI about your brand appeared first on MarTech.

It’s as clear as you want your data to be – this is the time to have a 360-view of your customer. You want to improve their journey and experience and protect their privacy. So where do we go from here?

When identifying a solution, marketers must first identify their pain points, develop a cohesive data strategy and then decide on the right technology.

To learn how some of the most successful marketers vetted and invested in the right technology, register and attend “Data Down the Drain? CDPs Bring Value to an Underutilized Asset,” presented by BlueConic.

Click here to view more MarTech webinars.

The post Don’t leave the future of your data in vendors’ hands appeared first on MarTech.

Kim Davis, editorial director at MarTech and Chris Garza, regional vice president, field and inside sales at Dun & Bradstreet, discuss how the buyer’s journey has transformed significantly in recent years, forcing marketers/sellers to reach customers in new ways.

Data will be the key to achieving success in this new reality.

Tune into the podcast and learn:

• Where sales and marketing teams are finding success in this evolving landscape
• The role data plays in pivoting strategies to reach prospects
• Where the buyer’s journey is headed and how that will impact you

Guest

Chris Garza

Chris Garza is an experienced sales leader with over 12 years of successfully leading B2B-focused sales teams through transformation and growth. His experience leading inside, mid-market, and enterprise sales teams gives him a broad perspective of what it takes to succeed in an inside sales environment while also understanding how to build pipeline and relationships with the largest, most strategic customers. Garza has spent the last 15 years at Dun & Bradstreet, where he has held various different senior sales leadership roles. He currently leads the new business sales organization at Dun & Bradstreet, focused on selling across D&B’s Field and Inside Sales channels.

Moderator

Kim Davis

Kim Davis is the Editorial Director of MarTech. Born in London but a New Yorker for over two decades, Davis started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space. Prior to working in tech journalism, Davis was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication and as a music journalist.

Sponsor

Dun & Bradstreet, a leading global provider of business decisioning data and analytics, enables companies around the world to improve their business performance. Dun & Bradstreet’s Data Cloud fuels solutions and delivers insights that empower customers to accelerate revenue, lower cost, mitigate risk, and transform their businesses. Since 1841, companies of every size have relied on Dun & Bradstreet to help them manage risk and reveal opportunity. For more information on Dun & Bradstreet, please visit www.dnb.com.

The post Where is the buyer’s journey headed? appeared first on MarTech.

The art of attracting and selling to customers is undergoing rapid and unprecedented transformation. To stay relevant and competitive, brands must keep up with technological advances and ever-evolving consumer behavior.

However, adapting to these changes is not straightforward, as each generation has unique needs, values and expectations that brands need to consider when developing marketing strategies.

Join marketing experts from Zeta Global as they reveal extensive research and insights into the newest consumer trends.

Register and attend “The Changing Face of Marketing: Connecting with Gen X, Millennials and Gen Z,” presented by Zeta Global.

Click here to view more MarTech webinars.

The post Create tailored customer experiences for each generation appeared first on MarTech.

“If you are in a decision-making role you are probably tasked with at least evaluating whether this is a technology that you need to pay attention to,” said Milicevic. “And if you’re a practitioner, you very likely have to come up to speed on how to use it and on whether it’s relevant to your company.”

What is a DCR?

“It’s a technology that creates a secure, collaborative environment where two or more parties can use data for specific, mutually agreed upon purposes while eliminating exposure of that data to other parties,” said Milicevic, citing the IAB.

Why use a DCR?

“The key innovation here is how potentially sensitive customer data sets are handled,” Milicevic explained. “[Marketers] simply need a better, more secure environment to collaborate with potentially sensitive data sets — first-party data sets in particular.”

First-party data is becoming increasingly scarce with the introduction of privacy regulations like GDPR and CCPA, as well as the phasing out of third party cookies by Google and other privacy actions by major tech companies along the lines of Apple’s Mail Privacy Protection (MPP) program.

Dig deeper: How companies are leveraging data clean rooms as cookies vanish

Who uses DCRs?

DCRs can be used by brands, agencies and publishers. The catch is that these organizations should already have a high level of data maturity — they’ve made prior investments in data technology and have substantial teams to work with the technology. This means that right now the technology favors larger companies.

“Process cost and maturity are two significant gating factors that currently put data clean rooms as a super-premium or ‘luxury’ solution,” Milicevic said.

How much does it cost to use a DCR?

Two-thirds of DCR users have spent a minimum of $200,000 on the technology, and a quarter of those surveyed by the IAB have spent over $500,000, according to Milicevic.

The annual cost can go up over $2 million annually when adding in privacy protection tools and other technology that makes the DCR usable.

What are current and emerging use cases for DCRs?

Current uses for DCRs include

• Data privacy compliance;
• Data anonymization;
• Data cleansing and normalization and
• Data transformation and enrichment.

Emerging use cases include:

• Attribution;
• ROI measurement and modeling;
• Mixed media modeling and
• Predictive analytics.

“In addition to privacy safety and the ability to combine first-party data sets is…being able to do very advanced analytics in a much easier way,” said Milicevic. “If you are a data scientist or have data scientists on your team, you’ve probably heard quite a few complaints about how long it takes to get data into a shape where it can be analyzed. Data clean rooms will reduce this complexity significantly for a lot of advanced analytics.”

Where does a DCR fit in your stack?

Generally, the DCR fits between the organization’s data layer and activation layer.

Here is a basic map that is by no means exhaustive:

At the bottom of the stack is the data infrastructure layer that might include a data warehouse, data lake or similar container. Data governance and identity tools also live in this layer.

Sitting above that is what Milicevic calls the “trust layer,” and that’s where the DCR is. Also in the trust layer are decisioning tools that use data to inform activation found in the layer above it. The activation layer includes all advertising activations and other tools like CDPs that can have activation capabilities.

“What’s particularly attractive about data clean rooms is that they pull out the business logic that used to previously live either in the data infrastructure or activation layers…and now it’s centralizing it,” said Milicevic.

Register and watch The MarTech Conference here.

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Customer Data Platforms (CDPs) are here to stay and have become a must-have element of the martech stack.

The latest MarTech Intelligence Report, Customer Data Platforms: A Marketer’s Guide, found that interest in CDPs increased 32% last year. Many respondents listed CDPs as a high-priority technology investment. However, according to Forrester, nearly 90% of marketers say their CDP doesn’t meet their needs.

So where do we go from here?

Register and attend “Data Down the Drain? CDPs Bring Value to an Underutilized Asset,” presented by BlueConic.

Click here to view more MarTech webinars.

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1. Discovery

The discovery stage involves learning, documentation and shared learning phases.

Learning phase

Discovery shouldn’t feel like halting progress, although it’s a good time to reflect and re-set. The first exercise is to step in the customers’ shoes. Find the journeys they take. Where do they get stuck? When do they reach “goals” (across funnels)? 

Enter the website the most common way, based on site analytics data (direct, search, referral, email, etc.). When needed, clear your cookies so you are treated as a first-time visitor. Continue through the site, taking action when prompted. 

Documentation phase

Document the steps on the journey. This should help you know the success metric you want media to drive (i.e., form submissions, orders, or event visits/time spent on certain pages). Hopefully, existing campaigns are using these milestones. If not, start tracking and monitoring. Then, match these steps with where data is collected and which systems store this data. 

Is there a CRM system collecting leads? What ecommerce tool is used, and what other systems does it send to? Map this out as another documentation guide. 

Lastly, collaborate with other departments to share these findings and ensure data is usable in marketing tools. IT departments and product teams are the most common teams we collaborate with during this phase. 

There could be ongoing workstreams that you can assist with, or most commonly, you will need assistance from other departments to implement.

Shared learnings

A thorough discovery process can take time. For a banking client, discovery involved interviewing stakeholders across seven departments and auditing five of their marketing systems.

This resulted in creating various documentation, including a 70-page document with roadmaps, process recommendations, advice for setting up a center of excellence and reporting instructions. While it took months to create, the client successfully implemented and used the CDP for the last two years. 

We often execute a maturity audit to find an area of focus for the client (either a marketing channel they haven’t tapped into or a feature within an existing tool they are paying for). This involves auditing their marketing system and collaborating with our main stakeholders to understand goals. 

A technology client was underutilizing content features in their CDP. We created a taxonomy of their products to input so they could create audiences off content affinity and recommend content to on-site users. 

Dig deeper: Why we care about data-driven marketing

2. Implementation

Planning

With your objectives known from discovery, you can build a plan around the components required to achieve those objectives. Remember, the “goals” found during your discovery should be your KPIs. Consider the following areas during this phase:

• Channel.
• Audience.
• Messaging.
• Timing.
• Goals. 

Ensure your team has adequate resources for sizing and planning audiences to give budget recommendations. Your media buyers can handle the rest in the same way they typically buy addressable media. Overall, ensure you’re filling the gaps found during discovery. 

Set-up

This can involve setting up the connections between systems for our client and creating the audience logic or rules in the systems. For example:

• A CPG client wanted to increase memberships in their loyalty program. We designed a solution to import loyalty data fields into their CDP so they could create a seed audience for Facebook marketing. 
• Another financial publisher client wanted to run a cart abandonment campaign. They already had data available in their tools and utilized us to set up audiences correctly and do an A/B test. 

Testing should be a critical consideration during the planning phase — whether it’s A/B testing multiple versions of a creative or a hold-out group to validate the media spend. 

Planning a test ensures you have set up the campaigns in a way conducive to reporting results. Even without a test in place, this step guarantees you will understand the results. 

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

3. Measurement

The most data-centric part of the process is measurement. We recommend you think about measurement in two ways — delivery and performance. 

Delivery involves making sure the message is reaching the users. For email, look at deliverability metrics as the campaign is launching. This is important, especially when implementing an ESP. You want to ensure your rules in the new ESP match and your IP reputation remains strong. 

For paid campaigns, it’s looking at reach and how your budget is pacing compared to the remaining days left in the campaign. If channels are underdelivering, you can optimize that spend elsewhere.

4. Optimization

Creating benchmarks from previous performance will help you understand how the marketing efforts are performing. These benchmarks then allow you to optimize within and between channels. 

If a test is in place, have a checkpoint for when results will be statistically significant. Having a hold-out group for the cart abandonment campaign mentioned earlier allowed us to prove the value of sending the incremental email. 

And while low performance or stagnant results are disappointing, sometimes your biggest lesson learned comes from failures. Your next data-driven strategy can often be based on results from your existing campaigns. Set yourself up to have feedback loops to easily repeat these phases with each new plan. 

Final thoughts

This approach will unlock first-party data strategies you can utilize across your marketing channels. Going through this process will create a great reference for new employees and partners. Keep your strategies updated as your business evolves. 

The implementation and measurement phases should always be done for each planning cycle. Staying in the habit of doing so will result in a “continuous improvement” cycle. 

The post An approach to unlocking first-party data strategies appeared first on MarTech.

The art of attracting and selling to customers is undergoing rapid and unprecedented transformation. To stay relevant and competitive, brands must keep up with technological advances and ever-evolving consumer behavior.

However, adapting to these changes is not straightforward, as each generation has unique needs, values and expectations that brands need to consider when developing marketing strategies.

Join marketing experts from Zeta Global as they reveal extensive research and insights into the newest consumer trends.

Register and attend “The Changing Face of Marketing: Connecting with Gen X, Millennials and Gen Z,” presented by Zeta Global.

Click here to view more MarTech webinars.

The post How to connect with Gen X, Millennials and Gen Z appeared first on MarTech.

On May 2, designated as International Martech Day, Scott Brinker unveiled the latest edition of his marketing technology landscape. From 9,932 last year, the total number of solutions in the landscape has grown to 11,038. This represents something like a 7,000% growth in the space over the last 12 years.

One new feature launched alongside the landscape is the Marketing Technology Capability Heatmap. It reveals the most searched-for capabilities, with personalization at number one, and identifies the categories that offer them.

Presenting at Best of Breed Marketing Summit alongside Marketing Tribe founder Frans Riemersma, Brinker, VP platform eco-system at HubSpot, said: “On average, across companies of all sizes, you still have around 291 SaaS subscriptions. Even while there is consolidation of the industry in the economic environment of today, a motivation to rationalize stacks, we still see people using this very wide variety of tools.”

Dig deeper: Why marketers are replacing foundational martech

Other key trends. Among the trends identified within the new landscape, Brinker and Riemersma highlighted the following:

• Two-way data flows between data warehouses and front-line solutions like CDPs.
• Composability emerging in a number of categories including DXPs, commerce and CDPs.
• “AI as your co-pilot is real,” said Brinker; and it’s going to change the relationship between buyers and sellers because buyers will have AI too.
• Generative AI will extend the no-code revolution beyond marketing ops and power users to any marketing or business user.

Why we care. Everyone who knows marketing technology knows the marketing technology landscape. It’s an evolving but iconic image. It’s such a crowded graphic, however, that it simply became hard to read. This is why it’s so valuable that it has become, essentially, interactive. Registered users can explore the actual content of the landscape rather than just gaze on it in wonder.

The landscape is available at Chief Martec and can be sorted, filtered and queried at martechmap.com.

The post Scott Brinker unveils 2023 martech landscape appeared first on MarTech.