Becoming a Data-Driven Brand Strategist
Over the past 10 years, the field of behavioral economics has illustrated the irrationality of human behavior. In the book “Everybody Lies,” Seth Stephens-Davidowitz taught us that focus groups, surveys, and direct customer feedback are poor predictors of customer actions, because what people say they will do and what they actually do are two different things. Your customers have bias, and data can help you uncover their true course of action.
The field of data science represents a new frontier for marketing professionals. In today’s digital world, companies have access to massive amounts of customer activity data. In fact, for some marketers and startup executives, data is the most valuable tool in their brand strategy toolkit.
Even though ecommerce and SaaS brands have an advantage over non-digital distribution brands, companies like Walmart are launching money-losing programs that use people to perform personal shopping services in order to collect data on the buying habits of specific market segments. Walmart realized that Amazon is far ahead of them in data collection and are trying to catch up.
You probably won’t need to hire personal shoppers if you’re looking to become a brand strategist, but you will need a thorough understanding of how to use data to better understand and connect with your audience. And if you’re an existing marketer not yet using data to support your brand strategy, you’re still ahead of the curve if you begin leveraging data-driven decisions instead of relying on anecdotal evidence and guesswork.
Data Science Explained
Data science is a new business field that focuses on uncovering knowledge and insights from data in the areas of marketing, finance, human resources, operations and performance. It has evolved from the intersection of social science and statistics, encompassing information, computer science, and design. Data science is being driven by the technical advances of programming and the ability to access and manipulate large sets of data.
The technology that’s required to capture large sets of data and algorithmic development that’s required to analyze the data can be intimidating to many marketers and non-programmers. However, to keep it simple, think of data science as the use of data to uncover or confirm correct business decisions instead of guessing.
Chances are you’re already interacting with brands that heavily rely on data science to improve your experience with their brand. If you listen to streaming music, Pandora and Spotify use data science to recommend music to you based on your previous selections and ratings. Netflix mines the data about your movie viewing patterns to better understand your taste and make recommendations for you in the future. Amazon also relies on data science for product recommendations. Procter & Gamble uses time-series models to more clearly understand future demand for their non-technical products. Target uses data science to analyze customer segment behavior and create custom marketing messaging for different audiences. And Uber, Airbnb, and other leading tech companies all rely heavily on data science for marketing and product development.
If you’re new to data science remember this: it’s all about the meaning of the data rather than the raw data itself. While the typical data scientist is talking about things like SQL, Hadoop, Hive, R, Python, JSON and .csv files, someone from the product marketing side has to interpret that data in order to make proper business decisions. Understanding what data to collect, what it means, and how to apply that data to your brand strategy implementation and marketing decisions is what’s important — not the tech behind collecting the data (assuming, of course, that the data is accurate).
The Voice of Your Customer
Data can add value everywhere. As a representation of the voice of your customer, data is effectively a record of an action someone performed, whether it’s online during the purchase process, in your software, or in your retail store. Data represents decisions your customers made about what they can or cannot do with your offering. Collecting and analyzing data can turn those customer actions into stories that other people can understand.
As you’re thinking about the role of data and data science within your marketing activities, think about how data can uncover patterns and tell stories for your brand. Start by looking at how well your company uses data today:
- Are you currently collecting information about your brand?
- About your market?
- About your customers?
- Is your marketing driven by metrics?
- Do you perform tests and experiments for campaigns, messages, and creative and then optimize for the best performing results?
- Do you collect feedback from customers and measure their actions?
- Do you monitor your brand and score your results? Track customer engagement?
- Does your IT team assist the marketing team?
- Do you currently have budget for data and metrics?
Data is absolutely critical for most top-performing marketers today and it will be for your brand at some point in the future, if it isn’t already today. Here are some tips for how to get started.
Data Starting Point #1 — Core Unit Metrics
The place to start is with your core unit metrics. These metrics will depend on your industry, product or service, and your distribution channels, but in general, core unit metrics tell business people how much it costs to acquire each type of customer in each distribution channel for each type of campaign. This is called your customer acquisition cost, or CAC for short.
The formula for determining your CAC is simple: all of the cost spent on acquiring a specific group of customers divided by the number of customers acquired during the spend. For example, if you spent $10,000 in marketing last year that produced 100 customers, your CAC is $100 (which is $10,000/100). The hardest part of calculating CAC is obtaining the metrics. You’ll need to determine each component of your marketing spend, how to allocate it to specific products, channels, and campaigns, and how to track sales. Start simple for this with the largest grouping of customers (which could mean all customers, and begin segmenting from there). Picture high-level CAC, even if it’s an estimate, and then begin refining and iterating from there.
Next, calculate how much each customer is worth — which is your customer lifetime value (CLV). The formula for determining customer lifetime value is more complex than for CAC. It is typically calculated as the net present value of the profit you’ll earn from all of the customer’s purchases over time. You can calculate this for a single customer, a group of customers, or your entire customer base. It’s most valuable when you use it for similar groups of customers who use your products and services in a similar way (your segments). I recommend that for each segment, you determine how long an average customer stays with you (which is their “lifetime”), the number of average purchases they make over that lifetime, and the average amount of profit attached to each one of those purchases. Then add up those purchases and translate them into today’s dollars, and this provides you with the CLV.
Once you understand your customer acquisition cost, you can compare it to your customer lifetime value to determine whether you’re obtaining customers profitably or not. This will help guide your future marketing spend. Now, as a marketer, it might not be your responsibility to calculate these metrics, or you might not have access to all of these numbers. If this is the case, engage other people within your company who can help you lead the initiative to determine the core unit metrics.
Data Starting Point #2 — Brand Awareness
When the market is aware of your offering, customers are more likely to reach out to you or make a purchase when they have a need that your offering resolves. Brand awareness is critical for new market entrants and is the focus of many marketing teams’ efforts before they focus on customer conversion.
There’s no single metric for brand awareness, so use what is most relevant to your brand. To determine what percentage of the market is aware of you, surveys are often the best tools (for determining something straightforward like awareness, instead of asking about behaviors). However, you may need to use a combination of surveys and data collected from other sources, such as media coverage, social media, and metrics from email marketing.
Data Starting Point #3 — Brand Perception
Your brand perception is how customers view your brand, the image they hold in their mind. Brand perception narrows the focus from brand awareness. Studies show that about half of your brand perception is attributed to what you say about yourself and your brand, and the other half is about your customers’ interactions with your brand and what they share with their friends, colleagues, and social media followers. You can use surveys or brand monitoring software, such as online review tools, to help you understand your brand perception. A Net Promoter Score (NPS) survey can be a useful tool to give you a single metric.
Putting It All Together
Core unit metrics and brand awareness data deliver essential metrics that every good marketer needs to have. Additional brand perception data is going to help you optimize your marketing and sales activities. A data-driven brand strategist measures key marketing and sales metrics at each stage of customer acquisition and customer retention activities. These can provide insights that can affect your strategic decisions. Here are some examples: cost per lead, cost per click, cost per 1,000 for display advertising, conversion metrics for any type of action, inbound requests, demos, opportunities, cost per x where x is whatever you need it to be (such as an acquisition of a follower or some type of interaction), renewal rate, time to purchase, or any A/B test of ad creative messages or email subject lines.
For some, this can be a tremendous amount of work, so start small and focus on the key metrics in stages one and two. If you’re starting from scratch, decide on a specific sequence of actions for using data science while implementing your brand strategy. Include a high-level time frame, which could be 30 days, a quarter, six months, or a year, and assign an owner to it. Select what is most important to you and move slowly, one step at a time.
Becoming a data-driven brand strategist is a marathon, not a sprint. You’re building, rebuilding, or strengthening the foundation of your business, and that takes time, so keep your eyes on the horizon and improve your use of data each quarter.
