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Customer insight fundamentals: Data requirements for Customer Insights Reports

Our customer insight fundamentals blog series aims to unpack important components of effective customer data analysis, prediction, and activation strategies. This article was curated from Tia Martin, Director of Customer Success at Faraday.

Customer insight fundamentals data requirements blog

From aligning creative and messaging based on who your customers are, to monitoring how your customer base is shifting as it grows, having a solid understanding of the individuals that make up your customer base is critical to maintaining successful growth strategies.

What is a Customer Insights Report?

At Faraday, we generate a wide range of customer insights and deliver them to our clients in the form of Customer Insights Reports. These insights are interpretations of trends in human behavior over time. They are intended to be both informative and actionable.

I’ve coordinated customer insight discovery projects for dozens of consumer brands — it all starts with getting the right data together. Before diving specific data requirements, let’s take a look at how Customer Insights Reports are created.

What can Customer Insights Reports tell you about your customers?

Customer Insights Reports can reveal a wide range of meaningful trends and patterns about your customers. Some analyses include identifying what makes your customers stand out from the greater population or specific geographies, patterns in product preferences and shopping behaviors amongst key cohorts, what differentiates one-time purchasers and loyalists, etc.

There are a variety of ways in which companies can approach developing a Customer Insights Report, beginning with a qualitative approach that would include surveys and direct interviews. Another way to approach this would be through quantitative analysis, using factors such as actual purchase history or financial information. These methods can be used independently or combined to help strengthen marketing strategies.

Faraday focuses on the quantitative approach, by combining first-party customer data, (think purchase history) with third-party consumer data, (demographics, purchase history outside of this company, and more) to develop a holistic picture of who these customers are.

These reports are customized based on what types of first-party data are available, as well as the kinds of insights that are meaningful to your current objectives, as well as your business as a whole.

Data requirements for Customer Insights Reports

Third-party data is necessary to expand the breadth and depth of your customer insights. We’ve built our own consumer identity graph which is comprised of nearly 300 million U.S. consumers and includes demographic, property & purchasing data from about a dozen sources.

When it comes to developing a Customer Insights Report, the first step is getting the right first-party data to match into the Faraday Identity Graph (FIG), which allows us to enrich the data you already have on your customers with hundreds of additional attributes.

In order to match first-party customer data into FIG there are a few basic fields that are required, these include:

  • First Name
  • Last Name
  • Physical Address
  • Phone (optional)
  • Email (optional)

Technically that is all we need to match into FIG. However, when it comes to generating meaningful insights and fully leveraging our prediction platform, more data is preferred.

Additional first-party data required for deeper analysis

Specific information about your customers such as when they became a customer and what they purchased will allow us to build predictions and provide insights on specific behaviors and actions future customers will take.

Below are some examples of the types of additional data that help expand the depth of your insights:

  • When someone became a customer
  • Items purchased
  • Amount spent
  • Number of purchases
  • Transaction history (purchase dates, order value, products purchased, etc)
  • Product(s) purchased
  • Discount used
  • Purchase amount

The rule of thumb is the more data the better. The more information our models can train off of, or the larger data set to glean insights from will allow for much more meaningful results versus just being based on a random or predetermined set of data.

Learn more about Faraday's customer insight discovery solution.

Saylent Engage enhances targeting capabilities with Faraday AI

Faraday is pleased to announce a new partnership with Saylent, a software provider that interprets bank and credit union data to help them better understand their customers and discover opportunities for growth. Saylent’s mission aligns nicely with ours: to help consumer-facing businesses optimize their growth initiatives by bringing data science out of the lab and into the real world.

At the foundation of our partnership is the integration of our platform with Saylent Engage, their product that provides tools to increase customer engagement, and in turn, revenue for financial institutions. The native integration will enhance Engage’s predictive capabilities with a combination of Faraday’s third-party consumer data and advanced machine learning capabilities.


“There has never been a more important time for financial institutions to understand their customers and provide them with the right solutions when they need them. Combining Faraday’s AI-driven predictions with the strength of Engage’s targeted insights and actions will allow our clients to quickly gain a deeper understanding of key customer segments, increasing customer engagement and institutional growth through relevant communications." - Joe Mearn, Director of Product, Saylent.

AI-driven predictions (propensity to increase deposits, purchase specific products, churn, and more) will be embedded into Engage, enabling users to reach new segments of targeted customers with relevant messages and offers. These predictions are the product of machine learning models that train off of multiple terabytes of consumer data found in the Faraday Identity Graph and behavioral data captured by Engage.

Saylent Engage

“In an age where big banks have whole floors dedicated to data science, regional banks and credit unions need to find a way to leverage AI to deepen customer and member relations. Our partnership with Saylent will empower every financial institution regardless of size to do just this.” - Robbie Adler, Co-founder and Chief Strategy Officer at Faraday.

By incorporating the Faraday models and predictions into Engage, Saylent’s clients will now have the ability to gain a deeper understanding of their customers and predicted behaviors, allowing them to take action with better results. This expands the power of Engage to not only provide insights based on behaviors already taken, but to also highlight opportunities that would be unknown by analyzing transactional history alone.

Consider broader changes in your customer data when adjusting your SEO/SEM tactics

Marketers across the board (ourselves included) are trying to understand how to adapt to the effects of COVID-19 on peoples’ lives, as well as businesses’ operations.

Thankfully, most consumers and organizations seem to be following the CDC’s guidelines for protection and prevention. These necessary, rather drastic changes in consumers’ everyday lives have naturally sparked lots of uncertainty, which is being reflected in the information people are searching for online.

In researching these trends for our internal marketing purposes, I came across several articles from thought-leaders in the space sharing some pretty valuable recommendations, some of which I mention below. But I also realized that very few addressed one of the most important components of SEO/SEM strategy — analyzing and adapting to higher-level shifts in companies’ customer bases.

Several publishers are closely monitoring global search trends and have made recommendations to help marketers quickly pivot to minimize potential adverse effects on traffic and conversions in the short term. Here are a few noteworthy trends I’ve seen:

  • Search traffic is surging up for essential products, health and wellness, and food and recipe websites — Search Enging Journal. Organic traffic is down for most other industries, including retail, real estate, and travel — Neil Patel.
  • There have not been any major shifts in cost-per-click (CPC) for pay-per-click (PPC) campaigns. However, lower conversion rates across most industries are driving up average cost-per-acquisition (CPA) — Neil Patel.
  • Competition will decrease, which could result in a PPC cost reduction. This presents an opportunity for marketers to gain search presence and improve long-term ROI on their SEM initiatives — Neil Patel.
  • Amazon is focusing almost entirely on fulfilling essential products, temporarily — Search Engine Journal.

This article from Search Engine Land shares a few SEM pivoting strategies, including adding certain keywords as phrase-match negatives to ward off unwanted traffic. Search Engine Land highlights a few ways you can respond to shifts in search traffic in this article.

Given that we’re built around understanding “people data”, I want to share an additional perspective on how companies can make intelligent SEO/SEM decisions in this rapidly evolving environment.

Analyze and act on big-picture changes

As consumers’ spending habits change, companies will see shifts in their customer base. There may be a reduction in purchasing frequency in one cohort, an increase in the average order value (AOV) of another, and so forth. The ability to quickly identify and act on these big-picture changes is just as important as adapting to changes in the more granular aspects of their marketing strategy.

How marketers can use higher-level customer insights to guide their SEO/SEM strategies

  • Identify distinct customer cohorts, uncover shared attributes amongst those cohorts, and monitor their purchasing behaviors to guide keyword strategies.
  • Compare regional cohorts across several dimensions (e.g. housing density/property type, shopping behaviors, purchasing tendencies, etc) to personalize landing pages.
  • Assess each cohort’s predicted lifetime value (LTV) to improve budget allocation.

Find markets worthy of further investment

Before allocating time and budget to create customized website content or adjust bidding strategies, marketers should have a solid understanding of which markets contain the greatest opportunities for growth and positive ROI. Some of the customer insights mentioned above become even more valuable when computing market size and penetration.

How marketers can use predictive analytics to compute market opportunities

  • Use a predictive model to determine the total number of likely buyers in a market (market size).
  • Determine what percentage of likely buyers are already customers (market penetration).
  • Compute the predicted average LTV of likely buyers, determine how much value was extracted from existing customers, and how much potential value remains in the market (opportunity index).

Enhance tactics with another data perspective

Earlier, I mentioned that we work with “people data” — what I mean by that is third-party consumer data. Used properly, it can be immensely powerful and necessary to generate the types of customer insights and geospatial predictions mentioned in the last two sections. At this point, I’ve illustrated some of the advantages this type of data can bring to SEO/SEM strategies, but it can also enhance more granular tactics.

How marketers can optimize bidding strategies and landing page conversions with a third-party data perspective

  • Customize landing pages and keyword lists using insights generated from the analysis of high-value regional cohorts.
  • Take a more granular approach to geospatial analytics to pinpoint specific zip codes with dense populations of predicted high-LTV consumers. Then, use bid modifiers to raise or lower bids on target keywords in those areas.

Read our post about third-party data in marketing for more use cases and important privacy considerations.

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Read more here

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