Data Collaboration Platform Playbook On-Demand Get Yours Now

Jumpstart Your CPG Data Strategy

The consumer packaged goods (CPG) industry is one of the largest in the world, valued at well over $2 trillion. The CPG industry includes goods that people around the world consume and replenish or replace frequently; everything from food and beverages, household products, diapers, to toilet paper and beyond.

Regardless of how quickly these items are purchased and consumed, the CPG market is not only highly competitive, but also incredibly fragmented in terms of market saturation. Adding to this confusion are steep loyalty curves; customers are smart, and know they can switch brands or suppliers at any time to access cheaper or better products.

Over the past few years, it has become possible for consumers to buy almost any product through an overwhelming array of online, always-connected services, which have completely transformed the CPG industry. With the digitization of the industry, each interaction a CPG company has with its consumers can lead to an array of customer insights that can impact future marketing strategies.

For CPG marketers to run successful digital marketing campaigns, you must understand how to reach your most valuable consumers with accurate and up-to-date customer data. This guide aims to help CPG marketers identify the correct audience data needed to deliver your most successful marketing campaigns.

Step 1: Take Inventory of Your Current Customer Data

Step one in delivering successful marketing campaigns is to take inventory of the data you have available. If you are like many other marketers, you probably have millions of customer data points siloed off in multiple platforms. You may have online behavioral profiles from your website and mobile visitors, data from your customer loyalty programs, social engagement, demographics, product purchases, or purchase data you received from a retailer.

By combining all of this customer data in one place, you can have a holistic view of your most valuable customers and how to reach them. Technologies such as data collaboration platforms (DCPs) can be used to tie these disparate sources together into one platform, allowing you and your team to better understand your audiences to guide future campaigns and advertising.

Imagine knowing that fathers with young children tend to gravitate toward products focused on simplifying a task, whereas mothers with young children better respond to colorful messaging focused on the health impacts of that same organic product. You could use this information to personalize messaging and shopping experiences, all the while delivering better-suited product recommendations. These kinds of contextualized insights provided by DCPs allow you to not only improve the experience for your customers, but meet your companies goals.

The end result is a seamless, simple shopping experience for the customer when and where they are ready to buy your products, so they keep coming back for more.

Step 2: Assess Your Data Needs

Once you understand the data you have, you will be better prepared to source the remaining type of data needed to succeed. First, establish your goals. Are you looking to grow brand awareness for a new product? Are you looking to drive sales among existing customers? Are you looking to grow your consumer base beyond existing customers? Is scale the priority, or are you more concerned with specific messaging to a precise group? Do you have a mailer coming out soon, and are looking for accurate customer data to match it?

Depending on your goals, there are multiple options available if your first-party data doesn’t give you the data or scale needed to reach your campaign objectives, ranging from lookalike modeling to purchasing third-party data or building second-party data collaboration partnerships. Let’s take a closer look at each of these.

Finding New Customers with Lookalike Modeling

Lookalike modeling is an automated, always-on strategy that works from an original group of “seed” profiles, and uses machine learning to find new profiles that look and act like your original target. This gives you the ability to find new consumers who are more likely to interact with your brand based on their digital imprint.

For example, if you are selling razors online, your “seed” list might include your previous customers, or anyone who already visited the purchase confirmation page of your website. Rather than continuing to focus on these past purchasers, you want to find new consumers to become your customers as well. Working off of this group of previous converters, the machine learning will analyze this group and identify more potential customers (lookalikes) who are most likely to also purchase from you or interact with your brand.

If you are looking to find new prospective customers most likely to engage with your campaigns, lookalike modeling can be a great option.

Third-Party Data

If you’re looking to target millions of potential customers and only have a minimal amount of first-party data available, how do you fill in the gap? Finding the right third-party data partner may be a good alternative.

Third-party data is used by marketers around the world to help scale your campaigns. It can also be used to enrich what you know about your target audience by providing additional insights on what they are interested in beyond your own products and services.

What kind of third-party data is used for CPG campaigns? While there are unlimited third-party data segments that can be purchased, some of the most commonly used data segments by CPG marketers include:

  • Online behavioral profiles — Online behavioral data will tell you how your target audience spends their time, what products they are using, and even their preferred interaction with your brand.
  • Demographic profiles — On top of knowing what your audience wants, you need to identify who they are. What age and income levels are more likely to buy your product or from your brand? Knowing your audience’s demographic makeup incredibly important to the success of your brand.
  • Geographic data — Omni-channel, modern experiences also call for in-person, in-store insights just as much as digital. Through location information, you can discern what stores are closest to your customers, and correlate that information with where they visit and what they buy.
  • Transactional data: There are third-party data segments available that segment consumers by which brands and products they’ve purchased in the past (in-store or online) so that you can target those consumers online.
  • Purchase Intent data: You may also target potential consumers that are in-market for your specific products to gain new customers you might not have had access to previously.
  • CTV Viewership data: If you’re looking to reach the same audience across TV & digital, you can use CTV viewership data to get closer to customers. CTV viewership data allows you to reach the same audience that watches a particular CTV show or network on digital platforms.

Third-party data is a great option for marketers looking to increase the overall size and scale of their campaigns. While the above data types alone can be very valuable, many CPG companies have seen greater success by layering data types for an even more powerful punch. For example, if a CPG company is looking to reach Moms in the MidWest who have purchased their products in the past, they can layer on demographic, geographic and transactional data to reach their target consumers. Third-party data is a great option for marketers looking to increase the overall size and scale of their campaigns.

Use these audience targeting strategies for more insight on who best to target as a CPG brand,

Second-Party Data

While third-party data definitely has its advantages, it’s not your only option when looking for scale. Second-party data is a more transparent, relationship-driven option if you are interested in taking that route. It is essentially another company’s first-party data that they are sharing directly to you. Not generally available on open data exchanges, second-party data is another option for marketers who want more visibility into data sourcing, as you are getting it directly from the data owner, through a direct and transparent data collaboration.

Non-competing companies with similar audiences, such as a women’s haircare product and a women’s skincare company for example, might be interested in sharing data. Not only is there transparency regarding what data is shared and with whom, but a relationship is built between both parties that enables strong communication and common goals.

Step 3: Be Curious

If you do need to access additional second- or third-party data to fulfill or scale your campaigns, how can you be sure what you’re buying is right for your needs? Below is a list of suggested questions you may want to ask to be sure that you understand the quality of the third-party data you are purchasing.

Questions to Ask When Choosing a Data Provider

1) What are your core verticals/categories?

There are as many interest categories or verticals as you can dream of with third-party data, but not every data provider offers every one. Some examples include past purchases, purchase intent, health and medicine, interests, or entertainment. If you are looking for a niche segment, you need to find out how granular the segments are. Does your third-party data provider offer multiple data types within a specific category?

2) Where was the data collected?

What is the source of this data? Is the data collected offline vs. online? Is it sourced directly from another party, or has it been sent through a chain of different providers?

3) How often do you refresh your data?

If there’s one thing you don’t want, it’s stale data. Make sure you know how often this data is collected, cleaned, and refreshed.

4) Does it beat industry on-target benchmarks?

If purchasing demographic data, has this data been tested against industry benchmarks (Nielsen DAR or comScore vCE)? If so, how does it perform? If you have KPIs you’re trying to hit, you’ll want to make sure the data you are buying is accurate.

Step 4: Get Creative by Personalizing Your Messaging

With so many choices when shopping, consumers expect highly personalized and relevant experiences tailored to their interests and likes. Personalizing experiences in digital marketing refers to using your customer data collected from a variety of sources to offer personalized messaging or product recommendations to specific groups of customers.

Once you have identified your audience and gathered the right kind of audience data, it’s time to work with your creative team to design experiences specific to that audience. Your messaging on digital and mobile may be as different as your messaging for various audiences groups. For example, for a consumer who interacts mostly on mobile, you may want to think about how to engage with them using capabilities native to a mobile device — walking distance to the closest store, leveraging haptic technology to bring your experience to life, or increasing time spent with an interactive AR/VR experience that focuses on the benefits your product offers.

Your customer data can be used for product recommendations, personalized content, and creative messaging, to make it easier for your potential consumers to quickly and easily find the products that might be best suited for them. Using the example from earlier, if you know your consumers who are fathers react to messaging focus on simplifying tasks, you can create short videos showing how your product would simplify their daily routines.  

If you aren’t using all of the data you have available to you to offer a personalized experience to your potential customers, your campaigns may be falling flat by not creating a relationship with your customers.

Step 5: Test, Measure, and Refine

Test your audiences and messaging before running fall-scale campaigns. Make sure they are working before going big. Use these learnings to make adjustments before launching your full campaign.

If you’re using a DCP, you can dive into campaign insights, and how specific audiences performed throughout different campaigns. Maybe the content from one campaign performed better with one audience over another. Through A/B testing, you can test those two audiences, analyze the insights, and make more informed advertising and business decisions for further campaigns.

Once a campaign has ended, the work is not over. It’s time to take a closer look at the performance, and start the cycle again to find out what is working best for your team. There is no one-size-fits-all solution in digital marketing, and what works for some may not work for you. So be curious, creative, and flexible. CPG marketing is constantly changing, thanks to seasonality, the economy and social changes. Your messaging and strategy needs to change as well.


Successful digital marketing campaigns rely on accurate, up-to-date customer data. As more purchases move online, each interaction offers CPG marketers the chance to collect valuable customer data, and use that data to inform their marketing efforts.

Lotame Precision Audiences is a new set of accurate and high-quality demographic audience data that helps you improve targeting, insight collection and analytics. Through a curated and verified process — carried out by our highly skilled team of data scientists — this data has been tested to exceed industry benchmarks for on-target accuracy .by up to 100%. Major CPG brands have already used Precision Audiences to reach their target audiences accurately and efficiently. 

Are you taking advantage of the huge amount of customer data available to you, to fuel high-performing CPG campaigns? Not sure where to start? Lotame’s team of data experts are ready to work with you to find a solution that fits your current and future needs. Contact us to schedule a time to talk.