We all like to think of ourselves as spontaneous creatures, casual, cool, easy like Sunday morning. The kind of people that can hop on a plane at the spur of the moment. Well, that’s optimistic. First try to order your lunch half an hour later than usual, and maybe take it from there?

You get the point. We are, regardless of how much we don’t want to be, predictable beings. For us marketers, that’s a comfort. And an advantage, that we must know how to use in our favor, like in the case of the most important purchase of all – the second one.

The Door is Already Open

In most e-commerce brands, the majority of resources are spent on bringing in new and additional traffic and only afterwards they invest in converting the existing leads. Succeeding in converting the lead to a customer is great, but oftentimes we are stuck with about 70-90% of first time customers who never make it to a second order. That’s a huge waste of potential.

One of our tools to measure true growth and true retention is to lower our one timer rate by growing our second timer rate. And think about it – it should be easy; these customers already like us, they already spent money on our brand. All we need to do is improve our personalized marketing outreach, that is based on their unique behavior, and we can significantly increase our second timer rate.

In this blog we’ll teach you a simple DIY method to help turn these one timers into “more timers”. We’ll learn how to do that by analyzing the patterns of our returning customers and categorizing the relevant marketing campaigns to send to your one timers, based on our knowledge of existing customers.

Every company has their own way of characterizing their products. If we know what characterized a specific first order that led to a second order, we can better understand how to personalize campaigns to first buyers with similar characteristics, that have yet to make a second order. Let’s begin:

Step #1 – Collecting

This DIY process can be applied to various types of companies. In our example we use a fictitious online clothing store.

For this analysis, we will collect data from all customers who made at least two purchases in the last year. The data needed from the first order is: department, category, brand, product name. For example: Shoes, boots, Timberland, light heritage cashmere flat boots.

We need the same data from the second order as well: department, category, brand, product name.

If a customer ordered more than 1 item in either one of the orders, we will create a line for each order, and in part 2 we’ll compare each item from the first order to each item from the second order.

Step #2 – Comparing

For each characteristic, we will create an excel chart. For each customer, we’ll define 2 columns (column B and C in the example below) in each chart: The first column will be the first order department (B), and the second column will be the second order department (C). In the fourth column (D), enter the formula = IF(B2=C2,1,0)

In this fourth column we’ll see the result of “1” if the value of the first order is equal to the value of the second order. Otherwise, we’ll get the result of “0”. See this example below for the department characteristic:

Step #3 – Schema

As we mentioned above, we’ll create an excel chart for each characteristic. Then, we’ll divide the total in column D (Is second order equal) by the number of rows in the specific chart, and that answer will be the percentage of customers who have ordered twice from the same characteristic.

Step #4 – Characterizing

For this step, we’ll take a look at the percentage result we got in column D (above) for each characteristic. If the result is between 40%-60%, we’ll recommend ignoring this characteristic when we begin creating our campaigns, since the results aren’t significant enough to create an action plan that will definitively have an effect on the correlation between the first and second order.

Now, we’ll be able to analyze the results we got from the 4 characteristics (department, category, brand, item) and understand the behavior of our customers during their second order. In accordance with our results, we’ll personalize the campaigns we send to customers who have yet to make a second purchase.

Here are a few examples:

Department = 80%

Category = 70%

Brand = 40%

Item = 20%

In this example, we’ll send a campaign with items from the same department and category as their first order, but we’ll recommend different brands and items.

Department = 40%

Category = 30%

Brand = 85%

Item = 10%

In this example, we would send a campaign with items from the same brand, but different categories.

Going the Extra Mile

After mastering that, we suggest you start drilling down into the numbers and charts. Here are two more vital ways to use the data:

1. If your company offers a wide variety of departments and items, we recommend doing this process within the department. For example: Choosing the woodwork department and noticing the category characteristic yields a 15% result, which is quite low, or going with the Gardening department, where the category is 85%. According to this example, we’ll send different kinds of campaigns for those who have purchased woodworking and those who have purchased gardening in their first purchase.

2. Regardless of the items your company offers, we recommend taking the process a few steps forward by creating sub-characteristics that will be able to give you a more granular perspective. For example: Analyzing first and second purchases for customers by looking at their geographical location, sex, age, or other attributes, that would help us have a deeper insight, and converting more first-time customers into second purchasers. Here’s a good demonstration:

In Conclusion

With Optimove’s vast experience across many ecommerce brands, we’ve been able to understand the importance of learning more about the behaviors of your current customers in order to better approach your new customers. This blog examined a simple way to practically understand the behaviors of your second timers in order to convert more first timers. Applying this method and evolving alongside it will help turn your one timers into loyal, lasting customers. Want to know more? don’t hesitate to give us a call.

Published on May 31st, 2018

Posted in

• Customer Retention
• Data Analysis
• DIY Hacks

Eilon Morgenstern

Eilon Morgenstern, a Product Manager at Optimove. He thoroughly enjoys turning data into valuable business insights and actionable lessons. Eilon holds a BSc in Industrial Engineering and Management from Tel Aviv University.