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Customer insight fundamentals: Understand your customer data to make better predictions

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 William Morris, Director of Data Science at Faraday.

Customer insight fundamentals customer data survey for predictions blog

At Faraday we make the distinction between descriptive statistics and predictive statistics. The latter frequently gets all the attention (“Seeing the future!”), but it can’t happen without the solid groundwork of the former. You have to understand what data you have at hand before you can leap into the predicted unknown.

The data survey

importance of data preparation quote

Customer insights — as practiced by Faraday — are an example of descriptive statistics, but there’s a lot more to consider when laying your data foundation. Most data scientists practice some version of what could be called a data survey; the goal is to surface meaningful patterns, gaps, and anomalies with an eye toward prediction. One of the more common ways for a data scientist to approach a survey is with a notebook, which allows for an exploration narrative, almost like a blog post with code and charts.

data survey notebook

For better or worse, notebooks can be freewheeling. While there’s no real limit to how far you can dig into certain datasets, at Faraday, there are a collection of standard metrics we look at in the process of building insights:

Acquisition time series

faraday recent orders persona screenshot

The pattern of how a group acquires members is usually essential in a data survey. This shows us if there’s a general upward trend, slowing enthusiasm, signals of seasonality, or spikes associated with specific actions like marketing campaigns or strategic discounting. A time series can also serve as the basis for any forecasting analysis.

Geographic distribution

united states data distribution map

Geography is a crucial indicator in a data survey. If a group is highly-concentrated in one region, it may not be the best seed from which to grow a national predictive model. The United States is a panoply of economic, racial, and cultural diversity, and geography is often the uniting factor in a host of demographic variables and indicators. Geography is also frequently the canary in the coal mine of statistical bias, and a good starting place from which to examine the implications of any future predictions.


Profile departure from baseline

customer insight comparison chart

As a component of Faraday’s customer insights analysis, we look at differences between a target group (e.g. “customers”) and a baseline group, like the whole - U.S. population. This allows us to gain a sense of the general profile of a customer and what makes them unique.

Next steps

Armed with a sense of what makes your data tick, you can confidently approach predictive analysis. Insights and surveys offer the full view you’ll need when adjusting a Bagged Decision Tree for regional weight, or tuning seasonality in a Prophet forecasting model.

Learn more about Faraday's customer insight discovery solution.

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.



What is customer lifecycle optimization and how does it work?

customer_lifecycle_optimization

Revenue and the customer lifecycle

For nearly every B2C company, revenue is linked to the customer lifecycle. Customer acquisition, engagement, and retention initiatives directly or indirectly impact revenue growth and sustainability by either reducing customer acquisition costs, increasing customer lifetime value (LTV), or ideally, both.

Effective marketing strategies revolve around the customer lifecycle; understanding key lifecycle stages, identifying events that are likely to trigger transitions between stages, and finding ways to optimize interactions across the lifecycle.

Naturally, the more you know about your customers and how to influence them to transition into a desired stage, the more efficiently you can grow and sustain revenue with the resources at your disposal.

Simply put, that's what customer lifecycle optimization is all about: leveraging rich customer data and predictive analytics techniques to generate insights and make predictions that measurably improve outcomes at each stage of the customer lifecycle.

Customer lifecycle optimization (a 4-step practice)

Customer lifecycle optimization (CLO) is a practice. As with other practices, CLO involves a series of prescribed steps to be done effectively. While specific lifecycle stage names, data sources, and analytics techniques will vary depending on your industry, company, and objectives, the canonical CLO process comprises four steps: lifecycle mapping; data discovery; predictive groundwork; and implementation and action.

1) Lifecycle mapping

The first step in any CLO initiative is always identifying and defining key lifecycle stages and transitions between stages. Mapping these stages and transitions to a uniform customer lifecycle is crucial to uncovering rich, predictive data.


The uniform B2C customer lifecycle:

B2C_customer_lifecycle

While terminology will change from business to business, we found that this formulation is rich enough to capture important boundaries, yet simple enough to avoid stages with ambiguous transitions.

Think about your customer lifecycle. How are stages defined? Which attributes qualify individuals to be placed in those stages? What events trigger transitions between stages?

Feel free to use the following table to help organize your findings:

Lifecycle_mapping_table

Note: The Customer lifecycle optimization whitepaper dives deeper into transitions and litmus tests. It's free to download.

2) Data discovery

At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage. Individuals will also have different attributes, which help determine whether or not they belong in any given stage.

Go through each cell of your customer lifecycle map and think about which events or attributes in your data could be used to trigger a transition or pass the litmus test. Then, think about where you can find that data. Is it in your ESP, CRM, or a custom data warehouse?

3) Predictive groundwork

Once you've formalized your lifecycle and its representation in data, you can start recognizing patterns and eventually predict outcomes. Imagine loading up your converted leads (customers in "retention and expansion") alongside your stale leads that never converted. What differences can you find?

This comparison analysis is especially effective when you've added depth and breadth to your existing data.

As you experiment with this type of pattern recognition, you'll quickly realize that it's the kind of thing that computers do very well. That's where machine learning comes in handy.

4) Guided outreach

Whether you want to increase conversion rates from lead generation campaigns or reduce churn from existing customers, properly targeted outreach is essential to engaging and motivating the right leads and customers to take a desired action.

Outbound communication is the strongest and most versatile intervention at an organization's disposal to compel progress and therefore expand revenue. This includes individually targeted digital advertising, a form of direct outreach.

Consider the following examples of outreach initiatives:

Customer_lifecycle_outreach_intervention

Having identified a stage transition you'd like to motivate with an outreach intervention, the question becomes, "Who do I reach?" Regardless of the desired transition, the general technique is called audience expansion, also known as "lookalike" audiences.

To apply the audience expansion technique, we must always identify the audience and a set of candidates with similar characteristics and attributes.

With these groups defined, the next step is to apply your predictive groundwork. This could involve using patterns you identified in your data to look for similar opportunities among your candidates, or in more advanced cases, using artificial intelligence to build a predictive model trained to discriminate between likely and unlikely transitioners.

Finally, you will be left with a well-defined group of candidates likely to transition into a desired lifecycle stage when reached with relevant content.

Putting it all together

To summarize the CLO practice, it's useful to recall the original motivation: leveraging rich data and statistical predictions to optimize revenue-building initiatives throughout the customer lifecycle. This means motivating transitions from one stage to the next.

At this point, you should have good understanding of what CLO is and the four canonical steps involved. For a deeper dive into specific data requirements and predictive analysis techniques, download the whitepaper below.

Customer lifecycle optimization whitepaper