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 Robbie Adler, Co-founder and Chief Strategy Officer at Faraday.

I feel like "turning insights into action” is the one of the most overused terms when it comes to marketing software data science services. Truth is, data and insights are relatively useless if your teams aren't prepared to act on them. So perhaps it's a bit ironic, but I want to focus on some critical learnings we've picked up around deploying customer insights and predictions at scale.

Set the right foundation and expectations

If you’re going to make an investment in machine learning and seek actionable insights from your customer data, you should not be looking for quick wins, done on the cheap. This doesn’t mean you need a team of data scientists or have to bite off a six or seven figure annual commitment, but it does mean you should approach the effort strategically.

At Faraday, we’ve spent a lot of time identifying what's needed at the foundation of an effective data science strategy for consumer brands (my colleague Tia covers data requirements in detail), but as a quick summary, we’d bucket them as:

1. Identify your data sources.
2. Identify the questions you are seeking to answer and why. Curiosity should not be the driver of these questions, but, rather, a clear path to action: "If I knew X, I would do Y."
3. Identify the members of your team who will need to be involved in and supportive of the effort. In our work, these team members are most commonly involved in performance and field marketing, customer engagement, marketing strategy, and data science.

Build a solid data science framework

Once you have the right foundation in place, it’s critical you take the necessary steps to build on this. My colleague Bill chronicles how we approach predictive customer analytics for our clients, but to summarize:

1. Survey your data. Avoid the “garbage in, garbage out” problem.
2. Validate your models and insights prior to activation.
3. Establish the integrations you need to support ongoing learnings and ease the path to activation. For us common integrations are data sources (e.g. data warehouses, customer data platforms (CDP), and CRM systems) and data destinations (e.g. ad platforms like Facebook and Google, marketing automation systems, and CDPs).
4. Democratize access, so you’re not solely dependent on a unicorn, otherwise known as a “data scientist,” for activation. Good integrations inherently facilitate democratization. One small test of whether you’ve democratized access is whether access requires knowledge of SQL, R, or other programming languages. If so, you haven’t democratized access.

Audit team access and business impact

So, it’s finally time to activate. How can you tell if you’ve built the right structure?

1. Are predictions and insights available in systems that your teams use in their day-to-day work? At Faraday, we’d want your performance marketer to see high-propensity audiences in Facebook, while we’d want your customer engagement marketer to see customer personas and product recommendations scores in their email marketing platform.
2. Are your predictions and insights being clearly leveraged to guide strategy? At Faraday, this often manifests as our clients personalizing ad creative and copy, deploying promotions strategically vs. broadly, launching new products targeted at a specific subset of existing customers, and/or entering new markets based on predicted propensity of its inhabitants vs. qualitative bias or preference.

Learn more about customer insight discovery at Faraday and check out our integrations.

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.

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

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.

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

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

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

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.

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.

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.

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.

Immediate impacts on search trends

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.

During times of economic uncertainty, consumer spending inevitably slows. While most companies feel the direct impact, these changes in behavior can present new opportunities for brands to strengthen relationships with their existing customers, and forge new ones by understanding how their brand, products, or services can bring comfort and reassurance to consumers in need.

In these turbulant times, none of us have all the answers. But we believe it's just as important as ever to lean into fundamental marketing principals when thinking of solutions to these new and unexpected challenges. We've put together a few thoughts that might help you nurture meaningful connections with your customers and find ways to continue to grow your brand.

Reflect consumers' motivations in your messaging

Consumers are viewing their buying patterns from a strategic point of view. The transactions they're making right now are likely related to health and wellness, nutrition, home essentials, and entertainment. These purchases probably aren't frivolous — they serve a purpose.

Consumers most likely want to add value to their lives now that they're cooped up at home. Ask yourself: How does your brand and messaging bring comfort into your customer's home? How does it help with potential cabin fever? Does it keep them comfortable? Busy? Intellectually stimulated? Take some things off their to-do list around the house? Ease their financial situation?

Consider the answers to these questions and incorporate them into your engagement initiatives. Your messaging should be empathetic and show that your brand recognizes why a consumer's purchasing behavior may be changing during this crisis.

A report published by the 4A's looked at the reception of brands' messaging around the coronavirus, finding that younger generations resonate with messaging that focuses on how the brand is supporting its community, while older generations are more interested in ads that speak to how the brand is keeping its customers safe.

Monitor changes in your customer base

Understanding how your customer base is evolving is critical to making sure your messaging continues to resonate with them, and enables you to make smarter decisions about where you should allocate your resources. This is particularly appropriate now as consumers turn to e-commerce to fulfill their shopping needs and brands try to optimize ad spend, messaging, and discounting strategies for both new and existing customer groups.

While this tactic will help short term sales and keep businesses going through what will be a financially challenging time for many, it's important to continue to track changes in your customer base to help meet long term sales goals. Forbes interviewed Kathy Bachmann, GM of Americas with the consultancy Analytic Partners Inc., and on the topic of what businesses can do to adjust plans for long-term projections she noted, “Some expect that consumers may not return exactly to their prior habits when the outbreak has passed … We recommend leveraging advanced analytics to support decisioning and reduce risk that can be run against a number of possible scenarios.”

Consider the systems your team has in place to monitor and engage your customer base. Can you react quickly to small changes happening now that will affect your business at a larger scale? Are you leveraging consumer insights to serve your customer base the way they expect you to?

Meet consumers where they are

With safety and quarantine measures being put in place globally to slow the spread of COVID-19, clearly event-based initiatives and physical retail are off the table. But this doesn't mean your brand needs to slow its advertising efforts.

Despite a significant drop in digital spend, marketers should still meet their customers where they are: online. Over the past few weeks, Facebook has reported a 50% increase in messaging usage as consumers look to their site for connection and news. Similarly, Pinterest reported that it saw an “all-time high around the world with more saves and searches on the platform than any other weekend in [its] history” as consumers turn to the platform for ideas of how they can spend their time at home. When consumers visit these platforms, your brand should be there too.

An argument against increasing digital spend — or even maintaining it — is that we are in the beginning of an economic downturn; consumers aren't buying enough to compel advertisers to spend the money to get in front of them. However, there is an upside to a decrease in the number of advertisers on social media, especially auction-based platforms where ad prices are likely to drop — you're more likely to land in front of your intended audience. The brands that are pulling back on digital spend are likely to lose traction with their customers as competitors replace their ad space.

Forbes reports that during past economic recessions, brands "that maintained or grew their ad spending increased sales and market share during the recession and afterwards." But you don't need to limit your ad spend to digital. Direct mail is still an option, and a good one. Forbes continues, “Studies have shown that direct mail advertising, which can provide greater short-term sales growth, increases during a recession.” And with a captive "at-home audience," getting into someone's mailbox may never be more effective.

All this being said, the analytics side of your strategy is just as important as the messaging and platforms you use. You should make sure your team has the appropriate systems in place so you can track how your ads are performing across various platforms and audiences.

Be intelligent about special promotions

With consumer spending slowing, many brands are offering widespread discounts. You can frame these offers in ways that shows your brand is trying to make its products accessible to consumers in an economically restricted time.

That being said, some brands are approaching it in a more utilitarian way, as they realize they have to sell their product in order to survive. Reflecting that honestly in their messaging, they're offering steep discounts site-wide.

As your own brand considers a strategy around discounting, consider your audience. What sort of products are they likely to gravitate toward right now? What would serve them best, while still allowing you to succeed?

Partner with like-minded brands

Everyone loves a good Instagram giveaway. Particularly now, large and small businesses alike are using giveaways as an opportunity for consumers to further explore their brands. Promoting giveaways — especially when partnering with like-minded businesses — increases brand awareness and audience engagement on social media platforms. This has potential to increase short term sales and introduce new customers to your products.

While this isn't a new strategy by any means, during this crisis community is of the utmost importance. Brands can contribute to their online communities by engaging their audiences and boosting morale through the positive messaging normally associated with giveaways.

Humanize your brand

Everyone is experiencing some level of fear, anxiety, and grief right now. There is comfort in knowing that there are real people on the other side of your brand's web page or Instagram feed. Humanize your brand and talk about the people who are working hard to bring your products to consumers.

Many companies have already addressed health-related safety measures their teams are taking to ship out orders to limit the possibility of exposure to the coronavirus. Not only does talking about these new processes reassure customers ordering products online, but it also reminds consumers that we're all having to make changes in our routines. We're all having to adjust to a new normal. There is an inherent level of community in that sentiment, bringing consumers closer to the brands they're relying on right now.