5 Simple Ways to Get Repeat Customers

The One-Time Buyer Problem

One-time buyers represent the largest overlooked opportunity for retail commerce marketers today. In fact, it has been that way as long as there have been retail sales.

Rather than focusing on the Trial (one-time purchase) population, organizations instead remain focused on acquisition as an avenue for growth.

Yet the grim reality is that the majority of those acquisitions lead to just a single purchase with no subsequent value.

Meanwhile, numerous published studies illustrate that the cost of acquiring a new customer is at least 5 times the cost of maintaining an existing customer. This results in a significant waste of time, money, and resources that could be focused on cultivating repeat customers.

Do you have a “one-time buyer problem?” The solution is within your reach by following this simple 5-step process.

Step One: Assemble Key Data for One-Time Buyers

First, we’ll share and discuss the “Simple Six” One-Time Buyer Data Points you can use to solve your one-time buyer problem. The good news is that most organizations either have them readily available, or can put them together with some help.

  • Valid customer record & contact information
    You must have a deduplicated single record of the customer – with all transactions rolled up under them – in order to know who your one-time buyers are (and aren’t). You also need sufficient personally identifying information (PII) and a method of contacting them. This includes their full name and a combination of postal/delivery address, phone, cell phone, and email. These are required to both complete a valid/merged customer record and provide a means to contact them with a personalized series of communications that are unique to their current state/situation and behavioral profile.
  • First Transaction Information, Buyer Source and Offer
    You will need to know when that first transaction took place, what was purchased, if a special offer was tendered, and the source of the customer. The number of items and the SKU’s in that critical first “trial order” are generally good predictors of the probability of a second transaction.
  • Transaction Amount
    The amount the buyer spent on the first order says more than the profitability (or lack thereof) on the first sale. It’s also indicative in many cases with the probability that they will order again in the future.
  • What The Buyer Purchased
    In addition to knowing how many items were in the order basket, we also need to know what they bought – including the Category and SKU of the item.
  • Date and Time of Transaction
    These are really two separate data points we split out and use for different purposes to solve the problem, but are usually captured in a single “time-date stamp” on the transaction. It tells us how much time has transpired since the first transaction so we can compare this buyer to all other buyers and determine whether or not they are more likely to buy again. Going beyond just “recency,” we can gauge one or more purchase windows for the individuals with a higher probability of a second transaction – or determine if they are just a lost opportunity.
  • The Profile of The Buyer
    Who is this buyer? Demographics and lifestyle intelligence give us an extra advantage in understanding who our one-time buyers are. Are they affluent or of limited means? Do they have children at home? Are they skewed towards Millennials, Generation X, or Boomers? Merely using a well-worn story about your customer is an assumption or shortcut that has proven detrimental in cracking the one-time buyer problem.

    “Our customer is young, rich and beautiful” may be a true statement, but what about the 1x buyers who are Boomers? Will you be relevant or tone deaf? The answer to this question helps determine whether or not you will move a trial buyer into loyalty and an evergreen stream of profitability.

These are very different customers, and our communications can be engineered in simple ways to spark them into a transaction and perform better when we speak in their voices and evidence our relevance to the customer. For example, personalizing an email’s subject line, hero shot, or the cellophane wrapper in a package can go a long way towards subsequent sales or more missed opportunities down the road.

Step Two: Rank Your One-Time Buyers by Aging

When we rank buyers by aging (a.k.a, recency), we’re determining how long it has been since they spent with us (note: this is not a ranking based on the age of the person). When we do this, we discover who has bought more recently and who hasn’t bought in a long time. This spectrum can be expressed as a distribution and in most databases, it’s not a normal distribution.

The Normal Distribution curve is a shortcut that most folks think about populations: it has a) total area under the curve +1 SD = 68.26%, +2SD’s = 95.44%.

However, your frequency of purchases almost certainly does not follow a “normal” distribution. It most likely either has a huge spike around 1 purchase, or is negatively skewed.

A real example of distribution by purchase shows that one-time buyers skew well below the often imagined “norm” or centerpoint in a normal distribution. This negative skewness illustrates that while it may seem “normal” that the typical customer buys a few times, we can clearly see in retail customer bases that most customers buy just one-time. This illustrates the need for addressing the problem proactively and early.

Notice also how the best spending customers that are the most loyal to the brand and have less time between their purchases, a requirement for a customer who spends a lot more. This is not to be confused with the fact that the majority of revenue is coming from the very large population of one-time buyers. Instead, it underscores the magnitude of the opportunity to sell again to your one-time buyers.

Step Three: Calculate the Window of Two-Time Buyer Purchases

Fortunately, we don’t have to solve the one-time buyer problem using only data about our one-time buyers. This is because all repeat buyers were once one-time buyers. Otherwise, it would be nearly impossible.

You almost certainly already have some two-time or more buyers. These individuals also have value in predicting when future purchases happen. The key is in the timing between the first and second purchase. Therefore, the goal is to understand both that behavior and its respective timing.

Step Four: Calculate The Inter-Order Purchase Time

We start identifying that window by looking at when historically our buyers made their second purchase. That’s measured as the difference between the date of the first purchase and the date of the second purchase in days. You’ll have to do this for every customer, next, compute the median number of days. We refer to this as the Golden Window for trial buyers.

Lastly, if we were to look at the distribution of the number of days between first and second purchase, we could determine if our repeat buyers might fall into different groups or clusters of behaviors that warrant further segmentation.

In the example below we have a distribution of customers by the days between purchase. In this particular example, there is a high probability opportunity to sell at about 114 days, and a second (even if its smaller) opportunity to sell at around twelve months.

The one year window is sometimes referred to as an anniversary purchase, and may coincide with a birthday or seasonal event (like travel for spring break). These cases in which more than one opportunity exist may indicate a dual universe with different types of customers, suggesting assignment to different communication groups to take full advantage of different buying behaviors.

Step Five: Campaign Execution

When we’re entering one of the spikes on the distribution, we see the probability to make the sale has increased based on the timing. This is an opportunity to sell, and we need to contact the customer and make a compelling offer. The conversion rate can be improved by leveraging the other data points we described in the “Simple Six” earlier, including the following:

  1. The initial source and offer that led the customer to her first purchase offers key insights on those offers and discounts that will work in the future.
  2. The Transaction Amount tells us if they are a high ticket buyer and if we should position more premium products. This also requires us to consider the number of items in the first cart. Many low cost items vs. a single high ticket item are indicators of different types of buyers. Your offer should distinguish between them.
  3. Your buyer’s demographic and psychographic profile is also an opportunity to tailor your creative and messaging around him/her. If we know our one-time buyer is a Millennial, Generation Z, or a “Global,” we’ll need to communicate differently than if they are a Boomer.

    Tailoring subject line, creative, message, and offer/call-to-action in either an email or the cover of a catalog or postcard has been shown to increase both response and revenue per campaign.

Some Customers Are Already “Lost”

There is a portion of your one-time buyers that stopped buying from your brand (or at least from you) long ago relative to those who bought a second time. These individuals have the lowest probability of buying again, yet should be the target of “reactivation campaigns.”

By personalizing a reactivation offer based on what we know of this one-time buyer, it increases the likelihood of obtaining a second purchase.  If the data shows that the customer has a high potential value, we would be foolish not to try and engage them. Systematic testing of reactivation offers by segments provides retailers with the best opportunity to increase purchases from one-time buyers.

Conclusion

By definition, one-time buyers are retailer’s largest customer retention opportunity. Increasing customer retention rates is one of the most significant opportunities that retailers have to improve profitability, according to many published studies. Thus, the one-time buyer problem is one worth solving.

While most retailers offer a welcome series of communications, the opportunity exists for many retailers to improve upon such series by applying the simple data elements and promotion timing insights mentioned in this article.

For more information on how you can address your one-time buyer opportunity, download our full whitepaper, “Solving the One-Time Buyer Problem” by filling out the submission form below.

Mike FerrantiFounder and Chief Executive Officer
Mike is the Founder and CEO of BuyerGenomics, brings 20 years of marketing, analytics and technology depth. He has developed solutions and software to major brand clients and niche marketers alike. Mike is a recognized thought leader in the database, search engine, email, and direct response marketing. He provides commentary and analysis to the media including Bloomberg TV, Brandweek, and DM News. Mike earned an MBA from The University at Albany and an Entrepreneurial Masters from the Massachusetts Institute of Technology.

2019-07-02T15:37:06+00:00