February 8, 2022
Jeff Shafer, Vice President, Data Science at Blend360
Think about what it was like to make your first sale. Maybe you didn’t make it, but somebody at your company did. A good salesperson can connect with the customer, understand their needs, and create a solution to delight them. A great salesperson can build an enterprise by repeating this approach.
High growth companies at all stages of maturity have figured out how to do this at scale. Analytics and data science play a valuable role as growth accelerants, enabling you to treat every customer as if they were your first. Below are some key principles to help you navigate rapid growth in your business where data and analytics are a driving force.
1. Set clear, measurable goals
You’ve probably heard this a thousand times. Yet it’s one of the frequent mistakes companies of all sizes make when setting customer strategy. Are you seeking to maximize the number of total customers? Total revenue? Gross profits? Gross profits per customer in the next 24 months net of acquisition cost? All four of these objectives require different strategies and will produce different outcomes. And these are just a few of myriad possibilities that often shift as a business matures.
Whichever you are pursuing today, it is important to define and articulate the strategy to the organization. Moreover, it is critical to understand how the strategy applies to different decision points in the customer lifecycle.
Let’s say you are running a streaming video service that offers a free tier and a paid tier. Your paid customers cost more to acquire, naturally, but they also generate subscription revenue and tend to watch more hours of programming and with that consume more ads. So, what are your growth goals? Maximizing accounts is simple: focus on the free product. Maximizing revenue would suggest promoting the paid offering. But a profit maximizing approach may be lead you to something entirely different. Here you may want to promote growth of the free product targeting consumers who will upgrade to paid plans later. (Hulu attempted all of these approaches before Disney consolidated it into a paid subscription bundle.)
Decision-makers at all levels must understand these goals to deploy the appropriate strategy:
· Define key success targets and clearly state the units of measure – e.g., total new accounts.
· Define required guardrails – e.g., we do not want users who only use the service once and abandon. Consider folding this into your target metric.
· Decide which metrics you will monitor but are not core to the strategy – e.g., hours of content viewed. You will not change decisions because of this.
· Align your predictive modelling approach with steps in the value chain of your success metric. You may want a model to predict signups and then a second to predict upgrades and perhaps a third to project viewership. These later models only become important as your strategy moves away from total new accounts.
2. Know your customer
Today’s plethora of data sources and data science techniques have unlocked the ability for companies to understand their consumers like never before. Leading customer-centric firms use segmentation, cluster analysis, predictive modelling, personalization tools and other technologies to acquire, retain and engage their consumers.
It’s just as important for high growth companies to invest in these capabilities as it is for larger, well-established players. Generating insights at the necessary pace and scale requires three basic components: a clear data strategy, systems that produce high quality data, and a skilled team of practitioners who understand it. Your ability to understand your customer and your marketplace is a powerful competitive advantage.
Be careful not to let anecdotes dictate the strategy. While it’s easy to recite some statistics – say 35% of Android users convert vs. 20%for iPhone – it’s far more important to understand your customer data holistically. A robust multidimensional segmentation will help frame your analysis and strategic thinking by taking in customer traits and behaviors.
Crucially important for fast growing companies is recognizing that early adopters are fundamentally different than customers you acquire later. These differences may or may not appear in basic demographics.
Here’s a starting framework for segmenting your customers:
· Tenure – When and how did you acquire them?
· Engagement – How frequently and how much do they purchase from you or otherwise engage?
· Needs – What problem are you solving for them and what products/services are they buying?
· Consumer Characteristics – Who are they? This is where demographics and other attributes come in, the richer the better.
3. Really, know your customer
Having an up-to-date snapshot of your consumer is critical if you want to personalize experiences, build loyalty, or perform basic analysis functions. If you have Tim Williams, Tim L. Williams, and Timothy Williams in your database, how many customers do you actually have? And which are high value repeat buyers, identified by your sophisticated machine learning models, and which are simply shopping with no intent to purchase?
This is a sticky problem anytime, but it will grow exponentially as your customer base expands. Hopefully in five years your customers base will number in the millions. But by then, some of those customer records will be five years old. Without proper maintenance, your valuable first-party customer data will become less accurate and potentially worthless.
Your organization must develop and maintain your first-party data assets:
· Conduct regular reviews of your customer database. Audit all points of customer data collection.
· Help the customer fill in blanks wherever possible, or at minimum, provide clear instructions and validation for what you want.
· Third-party APIs can help you pre-fill and properly validate terrestrial addresses at the point of collection. This saves the customer time and gives you better data.
· Engage a partner with expertise to help manage customer list hygiene. It is a waste of your time to try to do this yourself.
4. Invest in demand capture just as you would demand creation
Here’s a simple situation. Let’s say you’re a retailer with a budget of $10 million for digital media this quarter. Using historical benchmarks, you expect that campaign to produce 1 million visits to your website. Your user experience lead informs you that the typical visitor from paid media engages with a product 20% of the time, and then of those 30% ultimately purchase the product. That means you should expect 60,000 sales for a marketing cost per of$167.
But what if you could improve the site experience to turn more visitors into buyers? This is where investment in customer experience analytics and personalization comes in. By deploying site tools and aggressively testing, you’re able to create a 10% improvement in the engagement rate per visitor. If you think that doesn’t sound like much – think again. Simply capturing 10% more of the inbound demand would create the same increase in net sales – 6,000 – as you would by spending an additional $1 million on media. And that’s just for one quarter. Systematic improvements in the buyer journey have long-lasting effects on future demand, so every new media dollar will work harder.
Balancing investment among demand creation and demand capture is especially important for growth companies seeking to establish brand credibility. Former chairman and CEO of Procter & Gamble A.G. Lafley describes the “first moment of truth” for the consumer experiencing any brand. Think about the first 7-10 seconds in which you scan the store shelf for laundry detergent. Is the label compelling and easy to read? Is the package easy to handle? Tide can afford to fall down on these aspects occasionally because it’s a brand with tremendous equity in the market. But a new entrant to the aisle is significantly less likely to get the benefit of the doubt.
The same principles apply to your site experience. As a new market offering, you’re bringing less brand equity to the table. This makes friction points in the journey doubly damaging, because you not only lack the benefit of the doubt from the consumer but also can create negative equity from a poor experience. Don’t discount the long-term effects the latter will have on your future ability to engage that consumer.
Measuring and understanding the journey from demand stimulation to capture is a critical function for the analytics organization:
· Develop a measurement framework to identify the success metrics for each step.
· Ensure your media, site and sales systems are fully instrumented and in synch to capture all meaningful events in the journey.
· Establish benchmarks – ideally at the level of key customer segments – and monitor trends as close to real time as practical.
· Enforce rigor around testing and experimentation programs to enable sound decision making and build credibility in the organization.
· Test your systems and data pipelines for scale. If you run an ad on the Super Bowl, 120 million people will see it. Be prepared if they all come to your website at once.
5. Don’t over-optimize
Now that you’ve built a robust machine that’s collecting, cleansing, and analyzing data you’ll be able to optimize every step to acquiring profitable new customers. But wait. You don’t want to turn the knobs all the way to the right just because you can.
No doubt you want to place most of your investment in the market segments and consumers that generate the most profit. But it’s also important to keep learning and experimenting. If you’re not testing new market segments your growth will be limited to only those you know today. Consumer preferences and market conditions shift over time, so what was true last year may be less true now.
Imagine you are issuing a travel rewards credit card. Historically your “best” customer was Marie, a moderately high net worth executive, super prime credit who logs tens of thousands of miles on the road each year. You spend most of your time and budget trying to reach Marie with attractive offers. But what about Eric, the junior analyst who’s banking points for an aspirational trip to Tahiti with his fiancé? If your targeting focus is too narrow, you will miss Eric completely, and he’ll go on to be a great long-term customer for your competitor.
Often early-stage companies over-rotate on the customers they want rather than fully determining the market for customers who want to buy from them. To avoid falling into this trap:
· Allocate a dedicated budget for test and learn. Remember the 80/20 rule: 80% performance, 20% experimentation is a good rule of thumb.
· Measure performance results against performance goals and experimentation results against your learning agenda. Don’t hold experiments accountable for performance targets, and don’t dilute your performance figures with unproven tests.
· Never be afraid to try again. If the idea is promising, give it another shot under different conditions or in a different execution.
· Keep gathering data. Invest in some random samples to support future learning. You may uncover a hidden pocket of success.
Today’s data science capabilities put you ever closer to making that first sale again. When your organization is infused with high quality data science, you’re prepared to grow faster than your competitors. You know your customer. You know what will delight them. And you do it.
Blend360 helps fast growing companies harness the power of data science to accelerate their business. Connect with us to see how we can help you.