The predicted increase of over 30% is derived from a survey of 200 U.S. and European marketers, as well as interviews with industry experts.

Why we care. These findings underline the extent to which marketing will become increasingly data-driven in the months and years to come. In order to be data-driven at scale, marketing will rely more and more on an agile and well-integrated martech stack.

What’s more, the range of skills required within marketing organizations — or in close proximity — will have to include confidence in handling analytics and data.

Major challenges. Despite these prospects for growth, Winterberry listed four major challenges that continue to dog the data and analytics space:

• Quality of data and persistence of data silos.
• Conducting measurement despite “black boxes” (unreliable vendor performance data).
• The deprecation of third-party cookies.
• A shortage of talent.

Dig deeper: Marketing analytics: What it is and why marketers should care

A work in progress. Many marketing organizations recognize themselves as lacking maturity when it comes to data and analytics. 47% describe themselves as “emerging” or “progressing”: only 10% consider themselves “leaders.”

The main areas of interest as organizations develop the right muscles are audience intelligence, the customer journey, customer experience, creative and content, and media measurement and attribution.

“Organizations leading the charge in analytic decision-making have demonstrated that a cohesive strategy across data, technology, people, and processes is key to success,” said Michael Harrison, Winterberry Group Managing Partner, in a release.

The post Spend on marketing analytics and data infrastructure to grow sharply appeared first on MarTech.

Below, we will introduce the basic concepts around attribution modeling and ways to get the most out of it. 

Table of contents

• What is attribution modeling? 
• Why is attribution modeling so important?
• The different types of attribution models
• How to choose the right marketing attribution model
• Skepticism about the viability of attribution modeling
• Additional resources

Estimated reading time: 5 minutes

What is attribution modeling?

Your prospects and potential customers can take a range of pathways to interact with your content and make their way through the buyer’s journey. These pathways are specific touchpoints throughout that journey, such as:

• Opening an email.
• Clicking on an ad.
• Commenting on a social media post.

Attribution modeling in marketing allows you to determine how beneficial each of those touchpoints is along the buyer’s journey. With it in place, you can identify which marketing channel best helps to convert a lead from a mere browser to a loyal buyer.

By assigning a particular value based on interactions with a marketing channel, marketers can decide where time, energy and money should be spent. Knowing what touchpoint actually converts a lead can make a great difference for both sales and marketing professionals.

Why is attribution modeling so important?

Attribution modeling is critically important for several reasons. We will discuss them below in the form of some questions you can ask.

What improvements can I make to the buyer’s journey? 

When attribution modeling is in play, you will start to see what works and what does not. Underperforming areas can likely be improved, and you will dig deep into the data to find out why and how to do so.

What is the real ROI from a channel? 

Understanding the parts within the buyer’s journey that push your prospects to convert can help you see the channel’s or sub-channel’s value. It will also allow you to determine whether you should spend more (or less) of your efforts and resources there.

Can I craft better content for my ideal client? 

The answer, with attribution modeling, is a resounding yes. Tailor more of your marketing campaigns to the working channel(s) and the ideal client most likely to buy from you.

The different types of attribution models

There are several different types of attribution modeling. Each model looks at the various channels you are using but may apply varying degrees of weight to them.  

Multi-touch attribution modeling takes into account each touchpoint and channel within the buyer’s journey from start to finish. It will determine which channels were the most beneficial and effective in influencing a customer’s decision to convert.

First-touch attribution modeling focuses on the first touchpoint or channel that the client interacts with within the buyer’s journey.  

Last-touch attribution modeling focuses on the very last touchpoint or channel that a prospect entered before making the decision to convert.

Time-decay attribution modeling gives equal consideration to each touchpoint and channel but gives the highest points to the touchpoint that was interacted with closest to the conversion.

Cross-channel attribution modeling is a form of multi-touch modeling that looks both at touchpoints within each channel and also at how channels work together.

Linear attribution modeling is a form of multi-touch modeling that gives equal range and weight to all channels and touchpoints throughout the full cycle of the buyer’s journey. 

These attribution models can be used in marketing campaigns based on your pre-determined goals and KPIs. The answer to what is working and what is not working may change based on the type of attribution model that you’re using. 

How to choose the right marketing attribution model 

Before settling on an attribution model, you may want to test them to see which one works best for your campaigns. There isn’t necessarily one that stands out above all others.

Consider your campaign goals and how quickly those goals were reached. This can help you determine what model might best be used.

In addition, as your marketing campaigns evolve, you may need to switch the attribution model you use. Stay agile and allow the tool you use to move flexibly with you and your team. Here are a few questions you can ask before picking an attribution model:

• How many touchpoints are within the journey for the prospect?
• What are the goals of the campaign? 
• What does the overall funnel look like? 
• What is the end result or expectation of the campaign?
• Will I be using a tool, or can Google Analytics give me what I need?

Skepticism about the viability of attribution modeling

While most marketers find attribution modeling an essential part of their toolkit, some skeptical voices argue that traditional attribution modeling cannot cope with today’s vast proliferation of channels and touchpoints and the complexity of today’s customer journey.

“At the end of the day, people have to give up the fantasy that [marketing] can ever be completely predictable,” says marketing strategist Kathleen Schaub.

The skeptics say it is not feasible to hit a certain ROI target by a specific date. Instead, the suggestion is that marketing analytics should be treated as a kind of GPS, where routes and even destinations can be optimized along the way.

Dig deeper

Want to learn more? Here’s some further reading that may be useful: 

• How can we understand and fully embrace attribution modeling? Let’s find out in, Modeled behavior: A future-proofed new measurement strategy.
• Opinions on addressing attribution in digital and physical marketing campaigns are sparking a debate among marketing OGs and professionals.
• Do you need a marketing attribution and predictive analytics tool? It could be that you do.  

The post Why we care about marketing attribution modeling appeared first on MarTech.

Inevitably, this begs the question: should you employ your existing DWH as a customer data platform (CDP)? After all, when you re-use an existing component in your stack, you can save resources and avoid new risks.

But the story isn’t so simple, and multiple potential design patterns await. Ultimately, there’s a case for and against using your DWH as a CDP. Let’s dig deeper.

DWH as a CDP may not be right for you

There are several inherent problems with using a DWH as a CDP. The first is obvious: not all organizations have a DWH in place. Sometimes, an enterprise DWH team does not have the time or resources to support customer-centered use cases. Other enterprises effectively deploy a CDP as a quasi-data warehouse. (Not all CDPs can do this, but you get the point.)

Let’s say you have most or all your customer data in a DWH. The problem for many, if not most, enterprises is that the data isn’t accessible in a marketer-friendly way. Typically, an enterprise DWH is constructed to support analytics use cases, not activation use cases. This affects how the data is labeled, managed, related and governed internally. 

Recall that a DWH is essentially for storage and computing, which means data is stored in database tables with column names as attributes. You then write complex SQL statements to access that data. It’s unrealistic for your marketers to remember table and column names before they can create segments for activation. Or in other words, DWHs typically don’t support marketer self-service as most CDPs do. 

This also touches on a broader structural issue. DWHs aren’t typically designed to support real-time marketing use cases that many CDPs target. It can perform quick calculations, and you can schedule ingestion and processing to transpire at frequent intervals, but it is still not real-time. Similarly, with some exceptions, a DWH doesn’t want to act off raw data, whereas marketers often want to employ raw data (typically events) to trigger certain activations.

Finally, remember that data and the ability to access it don’t maketh a CDP. Most CDPs offer some subset of additional capabilities you won’t find in a DWH, such as:

• Event subsystem with triggering.
• Anonymous identity resolution.
• Marketer-friendly interface for segmentation.
• Segment activation profiles with connectors.
• Potentially testing, personalization and recommendation services.

A DWH alone will not provide these capabilities, so you will need to source these elsewhere. Of course, DWH vendors have sizable partner marketplaces. You can find many alternatives, but they’re not native and will require integration and support effort. 

Not surprisingly, then, there’s a lot of chatter about “composable CDPs” and the potential role of a DWH in that context. I’ve argued previously that composability is a spectrum, and you start losing benefits beyond a certain point. 

Having issued all these caveats, a DWH can play a role as part of a customer data stack, including:

• Doing away with a CDP by activating directly from the DWH. 
• Using the DWH as a quasi-CDP with a reverse ETL platform.
• Coexisting with a CDP.

Let’s look at these three design patterns.

1. Connecting marketing platforms directly to your DWH

This is perhaps the most extreme case I critiqued above, but some enterprises have made this work, especially in the pre-CDP era and platforms (like Snowflake with its broad ecosystem) are looking to try to solve this.

The idea here is that your engagement platform directly connects to push-pull data with a DWH. Many mature email and marketing automation platforms are natively wired to do this, albeit typically via batch push. Your marketers then use the messaging platform to create segments and send messages to those segments in the case of outbound marketing.

Imagine you had another marketing or engagement platform, a personalized website or ecommerce platform. Again you draw data from DWH, then employ the web application platform to create another set of segments for more targeted engagement.

Do you see the problem yet? There are two sets of segmentation interfaces already. What happens if you had 10 marketing platforms? 20? You will keep creating segments everywhere, so your omnichannel promise disappears. 

Finally, what if you had to add another marketing platform that did not support direct ingestion from a DWH?

2. Employ DWH with reverse-ETL tools

This approach solves several problems with the first pattern above. Notably, it allows (in theory) a non-DWH specialist to create universal segments virtually atop the DWH and activate multiple platforms. With transformation and a better connector framework, you can apply different label mappings and marketer-friendly data structures to different endpoints.

Here’s how it works. Reverse ETL platforms pull data from the DWH and send it to marketing platforms after any transformation. You can perform multiple transformations and send that data to several destinations simultaneously. You can even automate it and have exports run regularly at a predefined schedule.

But a copy of that data (or a subset of it) is actually copied over to target platforms, so you really don’t have just a single copy of data. Since the reverse-ETL platform does not have a copy of data, your required segments or audiences are always generated at query time (typically in batches). Then you export them over to destinations.

This is not a suitable approach if you want to have real-time triggers or always-on campaigns based on events. Sure, you can automate your exports at high frequency, but that’s not real-time. As you increase your export frequency, your costs will exponentially increase.

Also, while reverse-ETL tools provide a segmentation interface, they tend to be more technical and DataOps-focused rather than MOps-focused. Before declaring this a “business-friendly” solution suitable for marketer self-service, you must test it carefully.

3. DWH co-exists with CDP

Your enterprise DWH serves as a customer data infrastructure layer that supplies data to your CDP (among other endpoints). Many, if not most, CDPs now offer some capabilities to sync from DWH platforms, notably Snowflake.

There are variations in how these CDPs can co-exist with DWH. Most CDPs sync and duplicate data into their repository, whereas others (including reverse-ETL vendors) don’t make a copy. However, there could be trade-offs you need to consider before finalizing what works for you.

In general, we tend to see larger enterprises preferring this design pattern, albeit with wide variance around where such critical services as customer identity resolution ultimately reside. 

Dig deeper: Where should a CDP fit in your martech stack?

Wrap-up

DWH platforms play increasingly essential roles in martech stacks. However, you continue to have multiple architectural choices about which services you render within your data ecosystem.

I think it’s premature to rule out CDPs in your future. Each pattern has its trade-offs to keep in mind while evaluating your options. 

The post Should you use your data warehouse as your CDP? appeared first on MarTech.

Data analysis is supposed to replace hunches with facts. Brands don’t want to risk millions of campaign dollars on someone’s gut instinct. The marketer, ideally, has a goal, a clear threshold of success that must be crossed to achieve results. So how do you get there?

Data analysis is the “GPS.” The whole point of data analysis is to understand what is going on and to use that information to make the right decision. It’s “ready, aim, fire” (data, analysis, action). But sometimes the order gets mixed up, resulting in people drawing the wrong conclusions and acting on that basis. The process then becomes “ready, fire, aim”, or even more comically, “fire, aim, ready”.

“The biggest test of data is analytics,” said Mark Stouse, chairman and CEO of Proof Analytics.  “It contextualizes the data, making it extraordinarily difficult to manufacture conclusions, whereas data visualization alone makes it easy.”

Can data identify what’s causing something?

Can one gauge causality from data alone? Stouse believes not. Marketers can try by extrapolating from historical data, then check to see if this extrapolation was correct. “If everything is stable, extrapolation can work. But when the variety, volatility and velocity of change is great, extrapolation has zero value.”

“Data is indeed always about the past, and it has no innate ability to forecast. Past is not Prologue,” he continued. “But multivariable regression is the proven approach to taking data representing the relevant factors (the known knowns) — as well as some potentially important stuff (known unknowns) — and turning that into a calculated historical portrait of causality. That, in turn, creates a forecast against which you can understand the accuracy of the model vis-à-vis a comparison between forecast and actuals.”

Erica Magnotto, director of SEM at Accelerated Digital Media, sees the value of historical data, but only if there is room for retroactive perspective and predictive planning. “Forecasting campaign success should be based on trending data and performance like year-over-year and month-over-month. This should create close to accurate predictions on future success. If the forecasted data indicates a slower month or potential downturn in the market, optimizations can be made in real time to promote efficiency and conservative scale. If forecasting indicates a stronger month, then it’s time to start planning for scale, testing and additional campaign launches.”

Marketers should also be aware of hiccups in the model. Magnotto noted that there is a difference between normal “ebb and flow”’” of performance versus a crash/spike. “Data occurring outside of the normal margin of ebb and flow could indicate that immediate action in the account is necessary. Marketers should also not assume user behavior will always be consistent so it’s important to understand benchmark performance so abnormal user (or campaign) behavior can be detected,” she said.

Dig deeper: Marketing analytics: What it is and why marketers should care

What can marketers do?

Marketers must be analytical, open-minded, and humble at the same time. This alone can be a challenge when there are always some people who can be too self-assured, or fixated on the trivial at the expense of the substantive. Still, there are approaches to check mistakes before they happen.

Magnotto focused on knowing the data, the customer, and acknowledging reality. She offered this checklist for agencies, but the main points on it apply to brands too:

1. Understand basic excel/sheets principals and how to pivot large sets of data downloaded from any platform. 

2. Understand basic comparison formulas and default ways to look at data trends (month-over-month, year-over-year, period-over-period, week over week).

3. Have agreed upon primary KPIs and secondary KPIs with the client. 

4. Always speak the client’s language and incorporate the client’s source of truth data into reporting. This will ensure more productive conversations and help marketers navigate away from making mistakes or misreading performance. 

5) Know when to admit defeat in a campaign strategy. If a “great idea” is not working, then be comfortable allowing the data to speak for itself and changing strategies. 

6) Always QA reporting. Apply QA to formulas, timeframes, numbers, etc. If something looks too good to be true when analyzing data, it probably is. QA for mistakes that may be leading to that anomaly. 

Stouse stressed avoiding a fixed mindset. “Blindness to analytical reality is about choosing not to see, because what is there offers a challenge to what you believe.” he said. “The opposite of analysis is a certainty you have chosen and justified without any real basis except your own self-interest.  More mistake have been made in the name of certainty than anything else I can think of.”

The post Data plus analytics is the route to the truth appeared first on MarTech.

Marketo Engage and the B2B customer journey

Despite relatively little discussion of Marketo Engage during the main-stage keynotes, a look under the hood revealed a lot of activity surrounding Adobe’s offerings for B2B marketers. We asked Brian Glover to share some highlights. Glover is a senior director of product marketing at Adobe with special responsibility for Marketo, the B2B instance of Adobe Real-Time CDP and the B2B attribution solution Bizible.

Marketo Engage and Real-Time CDP working together

Adobe Real-Time CDP and Marketo Engage, working together, forms the foundation for B2B workflows in Experience Cloud, Glover said. “One of the announcements we made is that, within the Real-Time CDP you can create a list of accounts; you can send the list to multiple destinations, and one of those is Marketo Engage.” The advantage of bringing those accounts to Marketo is the ability to begin to engage with people in those accounts that are already known. “As you run paid media you start to bring in more of the other roles from a buying committee.”

Dynamic chat for B2B

Glover also highlighted Adobe’s evolving dynamic chat offering including new generative AI capabilities. “We announced that we are offering a full suite of conversational marketing capabilities, so for automating conversations on your website, and to pass a live conversation through to a sales rep, to be able to continue that conversation,” he explained.

Chat is also being embedded in things like lead forms. “In a lead form you can initiate a conversation or offer up the ability to book a sales meeting. We’re elevating the value that marketing can provide to sales by giving them more touchpoints for having a conversation rather than just capturing details and the prospect waiting for a sales person to reach out to them.”

A pause of hours or even days between an indication of interest and a follow-up by the vendor is a customer experience gap, Glover said. “By bringing dynamic chat into marketing auto-workflows we’re closing that gap.”

In the background to chatbot conversations lies the development of countless answers to frequently-posed questions. Generative AI will be used to help scale the drafting of responses — but all to be reviewed by human eyes before they go live.

Integrations between Marketo Engage and Workfront

Glover describes Adobe Workfront as “the command-and-control center of the content supply chain.” But many customers also use Workfront to manage the entire campaign development process, from strategy to assets, to reviews and approvals. This makes integration with Marketo Engage valuable.

“As the campaign is built out in Marketo, it sends the status back to Workfront; in Workfront it gives customers a single view of the status of a campaign so everyone has visibility. Teams can move faster, get more campaigns to market faster, which is important right now because a lot of teams are not growing as fast as they would like and many have budget and resource constraints.”

That B2B buyer journey

Glover agrees that the B2B buying experience is changing, becoming more digital, more self-serve and more omnichannel. And he has statistics that support this:

Digital-native millennials and zoomers now make up 65% of B2B buyer group members, and so the bar for what defines an acceptable experience continues to be raised. And now, 55% of B2B executives say their buying cycle time has increased over the previous year — anything that creates delays, confusion or uncertainty in the already complex B2B buying journey simply adds cost and risk to deals.

Adobe announces new innovations to drive B2B experience-led growth

Dynamic chat plays a role here too. “B2B buyers are absolutely looking to have more self-serve experiences,” he said. “It’s a digitally native population now that is participating in — and often leading — these buying committees. Automating conversations at scale, and making it easier to self-serve in terms of doing your own research, is one of the investments that we’re making.”

Bringing robust external data to Adobe Real-Time CDP

Adobe’s CDP offering remains one composable element of the overall Adobe Experience Platform suite. “In 2022 we updated our go-to-market to say everybody gets Experience Platform foundational capabilities, but they’re going to transact with Adobe on the applications,” explained Ryan Fleisch, head of product marketing for Real-Time CDP and Audience Manager.

So, Real-Time CDP is one of those applications. It was built natively on Experience Platform,” he continued, “as was Customer Journey Analytics, Journey Optimizer and some of the newer applications you’re seeing launched during Summit. The advantage is that, if I buy Real-Time CDP, I’m getting access to things like real-time profiles, a governance framework, AI models and many other services.”

In broad numbers, hundreds of brands around the world are using Adobe’s CDP for a variety of use cases. “We’re seeing a lot of brands that will start with Real-Time CDP and grow into other Adobe Experience applications. We’re also seeing a number of them adopt some of those simultaneously because they understand the natively connected benefits they get,” said Fleisch.

Data for specific use cases

One phenomenon that can be observed across a number of these enterprise-level CDPs that form part of larger marketing suites (Oracle’s Unity, for example) is that customers import data to support specific use cases. They don’t necessarily view the CDP as a repository for all customer data.

“If you’ve already done all the work to put your data in a warehouse or cloud storage system, we don’t want to make you duplicate those efforts,” Fleisch said. “You probably don’t need all that data readily available at your fingertips in a matter of milliseconds for the use cases you’d be powering here. So our approach is to think of a foundational layer of technology like a cloud data warehouse and think of Experience Platform and Real-Time CDP as an experience layer that sits on top of that.”

It’s also not necessary to copy all the use case data into the CDP. Adobe has the muscles to drill down into enterprise data storage locations and federate data from those systems.

Third-party data that isn’t from cookies

Despite widespread adoption of CDPs, many businesses are not yet ready to give up on DMPs, the solutions that deliver large quantities of third-party data and thus support new customer acquisition.

“If you trace the origins of CDPs they go back to about 2013 and they started out with just known customer data,” Fleisch reflected. “That was the primary use case.” But it’s not a use case that can make up for the loss of third-party cookies.

“Historically brands have used DMPs to just buy broad swaths of audiences and go target those. But we’ve seen an evolution in the CDP space and also in expectations for the space. We now have a privacy-safe way to bring in durable third-party data from partners like Epsilon, Merkle — and really any others of your choosing; this is an open framework. These companies have a long heritage in customer data from a variety of sources with consent attached to it. Being able to bring that into the CDP is fulfilling this request and vision of a single data management system that can take me from acquisition all the way through loyalty and everything in between.

The launch of use case playbooks

“In the past few years, CDPs have been the talk of the town,” said Fleisch, “but a lot of brands that adopted them are left wondering: What are the use cases I should be doing?” With many data sources feeding a CDP, brands can struggle to know what data to leverage for a particular use case.

“We want to make this easier for brands,” said Fleisch. “We’re launching use cases playbooks to actually give you guided workflows: Here’s how we would recommend populating audience segments, setting up journeys, campaigns, etc. You’re making time-to-value that much faster rather than having a blank canvas in front of you.”

Analytics for product use and engagement

Another announcement that almost got lost in the rush: The launch of Adobe Product Analytics as a complement to Adobe Customer Journey Analytics.

“With Customer Journey Analytics, as a brand you’re looking at customers and the different touchpoints they have — notification, mobile, web and any other ways you interact with them,” explained Haresh Kumar, head of strategy and product marketing for AEM. “With Product Analytics you are looking at product usage, the user journey. As a product manager, building products, you want to better understand where the value is, where users are spending more time.”

The persona Adobe is targeting with these new capabilities, said Kumar, is the product manager; the outcome the application is aimed at supporting is “product-led growth.”

It’s important to understand that, in a sense, Product Analytics is dealing only with digital products — only processing data generated by the software associated with the products themselves. The main-stage demonstration featured user engagement with a GM automobile — but it was specific to engagement with a software-driven dashboard component of the automobile.

But in a way, that’s the point. Many products these days, from cars to refrigerators to smart homes, have software components. Also, what’s provided is not just a retrospective look at how users are interacting with existing features, but guidance for future product development.

A new Adobe Express for enterprise

Finally, Kumar also highlighted the launch of Adobe Express for enterprise. Adobe Express allows the creation of a wide range of brand content without the need for training in design. The enterprise version integrates with the Adobe Experience Manager DAM and will also give access to the Firefly generative AI solution with safety guardrails for commercial use.

“That’s one big announcement,” said Kumar. “When you bring Adobe Express and Adobe Experience Manager assets together, you get not only the shared library of your assets but also genAI creation of more variations of content.”

The post Adobe’s roadmap for B2B, CDP and product analytics appeared first on MarTech.

This article explores the most sought-after skills in digital marketing and what they mean for marketing professionals and the industry.

Top skills marketing leaders look for when hiring

Up to 57% of marketing leaders prioritize analytics experience when hiring new talent, according to the State of Marketing 2023 report. As brands grow and become more data-driven, marketers who can effectively navigate and interpret data are highly valued.

Other skills that marketing leaders are hiring for and prioritizing are:

• Social media management (12%)
• Copywriting (9%)
• Video production (7%)
• Graphic design (6%)
• Search engine optimization (6%)
• Google Ads (2%) 

Although marketing analytics is specified, the ability to use data when managing social media communities, producing content and managing paid search marketing is also critical.

This shift towards prioritizing analytics experience reflects a growing recognition of data’s vital role in marketing strategy and decision-making. This makes sense for a few reasons. 

The need to demonstrate business value

Companies are tightening budgets to weather the current economic storm. At the same time, CMOs have been demanding that their marketing and PR teams demonstrate ROI from their programs. This is a trend that I’ve seen over the last 5 to 7 years.

Marketers were asked about their KPIs and how they plan to measure their programs’ performance in the same report, and 26% said that cost per acquisition/sale was the number one KPI, followed by:

• Social engagement (19%)
• Customer lifetime value (17%)
• Cost per impression (9%)
• Customer retention rate (9%)
• Cost per click (8%)
• Cost per lead (8%) 

These data points clarify that marketing leaders prioritize metrics that prove value. Outside of social engagement, these KPIs are all aligned with financial metrics.

Third-party cookie deprecation

Google plans to phase out third-party cookies in Chrome by 2024. Aside from rethinking audience targeting and focusing on first-party data, marketers must up their analytics skills to use the data effectively and draw meaningful insights.

Consumer privacy is also a significant consideration. Legislation, like the GDPR and CCPA, require companies to obtain explicit customer consent before collecting and using their data. Still today, 75% of marketers rely on third-party cookies.

Dig deeper: Why we care about compliance in marketing

Marketing budgets are on the rise

This year, over 50% of marketing leaders plan to increase budgets, but just 14% will make substantial investments, according to the same report. This is likely due to the uncertain financial times that have characterized the last 12 months.

However, despite these budget constraints, marketing leaders are still investing in data-driven strategies, such as:

• Investing in analytics tools.
• Hiring talent with analytics experience.
• Other initiatives to help them better understand their customers and engage them on a deeper level.

The demand for analytics skills will likely remain strong as marketing teams continue leveraging data to improve customer experience, drive sales and maximize ROI.

Per Gartner, almost 30% of the digital marketing budgets are being allocated to analytics across three functions: 

• Marketing data and analytics (9%)
• Customer analytics (8.8%)
• Marketing insights (8.3%)

While each function serves different purposes, all require an in-depth knowledge of data and analytics.

Marketing data and analytics is about performance

Hiring marketers with an analytics background is necessary to measure marketing performance better. Marketers should be able to analyze data from various channels such as paid search, email, display ads and social media to identify opportunities for improvement and provide actionable insights.

Knowledge of conversion rates, budget optimization, clickthrough rates and other performance metrics are critical. One mistake in reporting can result in millions of dollars of loss for brands.

Typically, someone working within this function would review the data and provide actionable insights after the campaign has ended.

Always-on customer analytics

Customer analytics is the process of collecting, analyzing and interpreting data about customers to better understand their behavior, preferences and needs. This involves using data sources such as customer transactions, demographics, web and social media metrics and customer feedback to identify patterns that inform business decisions.

In most cases, initiatives that require in-depth customer analysis using survey data happen quarterly or bi-annually. In large companies, this is usually outsourced to a research firm managed by an internal staff member with expertise in analytics.

Bringing the outside in with marketing insights

Marketing insights refer to the actionable knowledge gained from analyzing third-party marketing research from firms like Gartner, Forrester, Global Web Index, Kantar and Nielsen. These insights can help marketers and PR pros understand the current macro trends, consumer behavior and competitor activity in their industry.

This might be similar to customer analytics, but it’s more focused on industry trends and macro-level insights. Again, this helps marketers plan their strategies and understand the broader industry landscape.

Cultural trends and insights can also come in other ways. In the report, 31% of the marketers surveyed have designated cultural insights teams in-house. This approach is more expensive, given the cost of salaries and the current economic climate. But having an internal team can be beneficial in speed to insight and data ownership. 

Invest in the right resources to drive marketing ROI

Data and analytics are essential tools for modern marketers. Investing in the right talent, tools and processes helps you keep up with the competition. Building a team with different functions specializing in customer analytics, marketing insights and data and analytics is key to success. With the right talent and resources, brands can tap into valuable insights, drive revenue and maximize ROI.

The post What are the top skills you need for digital marketing? appeared first on MarTech.

“Our belief is that generative AI will enhance human ingenuity, not replace it,” said Shantanu Narayen introducing Adobe Firefly at Adobe Summit today. Firefly is a new group of generative AI models focused on creating images, video and text effects. Firefly uses generative AI with graphics tools like brushes, color gradients and video tools to speed up production and make it easier for creators to make high quality content. The videos and images projected on-screen during the two-hour Summit keynote had been generated by Firefly.

Adobe’s generative AI. The generative AI announcement predictably stole the show although there were some other new product announcements. “Over time,” said Narayen, “AI will help us reimagine every aspect of marketing.” He could not resist adding that Adobe has incorporated AI in its creative products for well over a decade.

The first Firefly tools are available today in beta. Narayen also emphasized that Adobe is seeking to protect human creators, both by developing a model for compensating for use of their work and by moving towards a global standard “Do not train” metadata tag that creators could use in an attempt to ward off AI infringements on their content.

Summit also saw the launch of Sensei GenAI, natively embedded into the Adobe Experience Platform, although it was not immediately clear how this would enhance the platform’s existing Sensei AI capabilities. Again, the aim seems to be to allow users to work with generative AI capabilities within Adobe Experience Cloud rather than using independent tools and migrating results to Adobe.

Why we care. In many ways it would be shocking if Adobe had not taken this route to incorporating generative AI into its platform. Although Adobe was one of the first marketing clouds, its roots are in iconic creative tools like Photoshop and Illustrator. The promise, of course, is that Adobe users will be able to reap the benefits of generative AI within the Adobe ecosystem, rather than have recourse to one of the many independent tools rushing to market.

Dig deeper: Adobe CEO: Make the digital economy personal

Adobe Product Analytics. If overshadowed by Firefly, Product Analytics was a significant announcement. The Adobe Experience Platform already includes, among its many components, Customer Journey Analytics. Product Analytics offers a similarly drillable dashboard by presenting product-related information such as user growth and engagement with features of products and trends. The aim is to align the product team with other teams working on aspects of customer experience.

Adobe also announced Adobe Mix Modeler, a new dashboard giving a cross-channel view of campaign performance allowing real-time optimization of channel spend.

Additional reporting by Chris Wood.

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In this article, we’ll take a closer look at nuances in data measurement and various analytics software in action.

Looking at nuances in data measurement  

Digital analytics tools were never intended to work as accounting systems or sales registers. They were made to collect and quantify interactional user data into easily usable insights and reports. Over the years, these tools’ data collection methods have evolved. In turn, the way specific data points are measured also changed. 

Let’s say you changed your tape measure from imperial (measuring in inches) to metric (measuring in centimeters). The length of a desk might be reported as 39.4 in one and 100 in the other. The length of the desk didn’t change, but how you measured it has. 

Try switching between different analytic tools. Often, you’ll see that your numbers may be different, but trend lines remain similar. Each tool counts things slightly differently; the same issue frequently applies when upgrading software.

At one point, unique users were counted by combining the total number of unique IP addresses that accessed a website in a given period. Eventually, organizations started using firewalls/proxy servers, requiring all internal users to access the internet with a single IP address. How unique IP addresses were counted didn’t change, but the count of unique users dropped dramatically.

Counting of unique users evolved into using a combination of IP address, OS and browser (type and version), then the addition of a persistent cookie to better estimate unique users. Once again, no matter how you count unique users, if the user cleared their cookies and cache or switched computers (office vs. home vs. phone), no analytics tool will have provided an exact number. Nowadays, tools take other factors into account when counting unique users.

Dig deeper: Data analytics: Your stack’s past and limitations

How to think of your analytics data

Your analytics software is imperfect because of many factors beyond its control. Users might be blocking cookies or other tracking methods. Internet blips might prevent data from reaching the data collection server. The best way to think of your analytics data is by viewing it as a poll of user activity.

Everyone is familiar with polls at election times. A typical U.S. presidential election poll surveys approximately 10,000 people (or less) out of 150+ million eligible voters (0.006% of voters). This is why when news broadcasters report on the poll results, you hear something along the lines of “This data is accurate within 4 percentage points 4 out of 5 times.” This equates to it being off by more than 4 percentage points 20% of the time.

When it comes to your digital analytics tools, most analytics professionals estimate the loss of data to be no more than 10% and most likely around 5%. How does this translate into data accuracy?

If your site received 10,000 sessions in a reporting period but for various reasons, you could only capture data on 9,000 sessions, your data would be accurate within a margin of error of less than 1%, 99 times out 100. 

In other words, 99 times out of 100, your data is accurate and 1 out of 100 times, it is off by more than 1%. Simply put, your data is accurate, but it is not perfect (precise) and will not match your sales records.

Such data is more than accurate enough to determine which marketing efforts — SEO, paid ads, sponsored posts, social media marketing, email marketing, etc. — are working and even which ones drive traffic versus drive sales.

Dig deeper: Don’t apply wishful thinking to your data

Analytics in action

While analytics data may be accurate, even being off a small percentage in precision can call your analysis into question. This is especially true when the difference between two data sources changes. 

The key is to monitor the data and, where possible, compare it. If there is a sudden change in accuracy, you need to investigate. For example, was your website recently changed? Was this change properly tagged to capture the data?

A client once added a pop-up to their Shopify account after an order was placed but before the thank you page was generated. Their analytics tool records sales only when the user receives the thank you page. 

With the pop-up in place, the order still went through, but many users didn’t click through the messaging. As a result, a large percentage of sales were suddenly not being captured as no thank you page was generated. There wouldn’t have been an issue if the pop-up appeared after the thank you page.

Below is an example of monitoring sales and orders between Shopify and Google Analytics 4 (GA4). We can see how much data is being lost because of various factors. Using Shopify’s analytics as a record of true sales and comparing it to data collected via GA4, we see the following:

The daily variations in total revenue and orders varied from virtually 0% to nearly 13%. Overall, in these 24 days, GA4 reported 5.6% less revenue and 5.7% fewer orders. This data is accurate, especially when applied to marketing efforts to see what drove the user to the site to make the purchases. 

Should this company use GA4 to report sales? 100% no! That’s what accounting software is for.

If your organization demands even more accurate data, there are methods to push data directly to most analytics tools (server side). This avoids issues with user browsers and cookies. 

While sales data may be more accurate, other soft measurement aspects of user interaction may drop (e.g., scroll tracking). This is a complex and time-consuming method to implement for most organizations. 

You must ask yourself, “is this extra effort necessary just to capture another 2-5% of sales revenue in my analytics reports?”

Understanding your analytics data

Everyone needs to have faith in their analytics data. The key is ensuring your analytics software is installed and configured correctly. Understand that it can’t capture everything. 

Your analytics software simply takes a poll with a sample size of over 90%. This makes the results highly accurate (on target), if not 100% precise (actual numbers).

The post Accuracy in digital analytics: What marketers need to know appeared first on MarTech.

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The post The Big Game brand lift results are in! appeared first on MarTech.

Haleon was created last year out of a joint venture between GSK’s and Pfizer’s consumer products, which include Advil, Excedrin, Robitussin, Tums and other household brands.

The company assembled an in-house team to use social media intelligence, or “social intelligence” — tools and strategies to understand what customers are saying about brands and how to leverage that intelligence to boost marketing efforts.

Dig deeper: Social media marketing guide for brands

Defining social media intelligence

First, Haleon had to define social media intelligence. It can mean different things in different organizations, so it’s important for each business to establish goals and benefits derived from social intelligence operations.

“Social intelligence is folding in all these different data sources and really trying to figure out what this data is actually going to do and what [it] tell us,” said Danny Gardner, analytics manager U.S. and North America social intelligence lead for Haleon, at The MarTech Conference.

Gardner and his team consider social intelligence as a more sophisticated version of social media monitoring and listening. Instead of just tracking different topics that consumers are talking about on social platforms, social intelligence draws insights from this data and ties the insights to marketing actions.

“Why does the business want to have social intelligence?” Gardner asked. “At its core, it’s insights. We’re able to act on this data and get to insights faster than any other team in the company.”

Brands that gather social intelligence have access to consumer opinions about their own products and also the competition. They also gain feedback about marketing campaigns and can learn more about their target audience.

Another benefit of social media intelligence is finding out where consumers say they are purchasing products. For Haleon, knowing if customers are talking about buying Advil at a Costco or through an online retailer helps the company develop an ecommerce strategy.

If consumers are speaking negatively about a brand on social, knowing this can help the brand execute a crisis management strategy, said Gardner.

Four social media intelligence categories

Social media is a vast space, and listening to it intelligently means having clear categories or “buckets” for the data.

Gardner and his team established four main buckets of data they wanted to gather through social channels. They wanted to analyze and gain insights from social conversions that related to their own portfolio of brands, competitor brands, broader topics related to using these products, and “macro and cultural” trends.

“There are a lot of trends that go on and things that happen in society that we’ve realized our consumers care a lot more about than our brands, and rightfully so,” said Gardner. “And so we took it upon ourselves years ago to build this into our remit.”

Building and scaling social media

Although Haleon only went live as an organization in 2022, their marketing strategy, including their approach to social intelligence, has been years in the making.

Here’s a timeline of the steps they took to implementing social intelligence tools and strategies.

“There was this large discovery phase around what data is available, how can we get to it, what does data mining look like, what vendors exist and what are their capabilities,” Gardner explained. “It was actually a couple years before I was hired that they started building the case that, hey, we actually think we might be able to do this in-house.”

Haleon also debated the pros and cons of building versus buying their solution, and eventually wound up settling on a suite of social intelligence tools developed by Meltwater.

Piloting social media intelligence during the pandemic

Just as Haleon was ready to test pilot some of their social media intelligence capabilities, the world changed. During the first years of the COVID-19 pandemic, many consumers upped their use of digital channels to purchase products and self-educate.

“We came out of our 12 month pilot, and at the end of the tunnel was COVID-19,” said Gardner. “And so this definitely accelerated the demand and interest for what social listening was and really catapulted us into the limelight…Social media was kind of the go-to for questions [consumers] didn’t have answers to.”

He added, “So at the time this is actually what inspired this macro trend tracking capability and we now know we can do this pretty well around our brands.”

As a result, Haleon has a better understanding of how consumers feel about their roster of brands. And they can join the conversation on larger issues in a way that’s relevant to their customers.

Register for The MarTech Conference here.

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