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How to Generate Sales from Inactive Customers and Boost E-commerceby@alexgenovese

How to Generate Sales from Inactive Customers and Boost E-commerce

by Alex GenoveseOctober 9th, 2020
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Online companies want to gain more understanding about their customers, to enhance better marketing strategies and uplift their business through data analysis. RFM analysis allows you to analyze important behaviours based on simple 3 dimensions: Recency, Frequency and Monetary. The output can be used to increase customer retention, customer engagement and targeted marketing. At the bottom of this article, you find the link for copying the marketing automation flow that I'm going to explain. The Marketing Automation flow starts when you "inactive" a contact and send an email for feedback request.

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What does marketing automation mean? Are activities planned triggered on user-generated events. Simple and clear. 

Events can be: click events, pageviews, user navigations, etc. All those events could be a hook to trigger a specific automated communication sent through different channel, such as email, push notification, browser push, app push notification or whatever. 

Why I'm going to focus on Inactive Customers? 

Acquiring a customer has a cost (CAC), trying to take back customers who are, to date, inactive has a much lower cost. Just consider the following data:

  • Acquisition cost is 5x higher than that of an already acquired customer
  • You have between 60%-70% more chance of being able to sell your product to a customer that you have already acquired, compared to a customer to acquire that a percentage between 5-20%.

So I need to put in place a tactic to "re-engage" them because they probably forgot us, or found more interesting products/services online from other competitors.

Where do we start

In order to deliver this tactics in the best possible way, it's really important to start from quantitative analysis, and never delegate to hypotheses

The customer base analysis is basically important because it represents your business potential in terms of sustainability and growth.

Side Note on advanced methods (machine learning)

Customer Base Segmentation (or clustering) could be analyzed using advanced machine learning algorithm that allow us to view segments with similar behaviors (K-Means algorithm).

For instance, I could use specific features in order to plot a specific customer segment which have purchased 2 times in the last 2 months a t-shirt and a pair of pants, and that are "most likely" looking for a shoe to match.

Information, needless to say, are a gold of mine for anyone with a profitable online business, today, this kind of analysis are reserved for only enterprise businesses.

Coming back to RFM

Online companies wants to gain more understanding about their customers, to enhance better marketing strategies and uplift their business through data analysis.

A pretty straightforward solution is RFM analysis, that allows you to analyze important behaviours based on simple 3 dimensions: Recency, Frequency and Monetary.

Recency is when their last purchase was, how often they’ve purchased in the past (frequency), and how much they’ve spent overall (monetary) in a span timeframe. The output can be used to increase customer retention, customer engagement and targeted marketing

The Marketing Automation

Considering to have 11 customer segments (active, inactive, VIP, loyal, etc.) it's possible to import them into lists in a Marketing Automation tools for boosting sales.

Personally, I've been using Active Campaign for years, because it allows you to manage the different automation flows by segments or sub-segments, adding dynamic tags, create customer scoring and integrate third party data through their tracking code.

Actionable hints therefore provides use of a marketing automation product to roll out – at the bottom of this article, you find the link for copying the marketing automation flow that I'm going to explain.

How to generate new sales

Once you've analyzed your customer base, you can export the inactive ones that could be a very large pool of the database.

Two options depending on your data type:

  • If you have behavioral and purchasing information: you can send an email more targeted, asking for a simple feedback (quantitative, as should be Net Promoter Score), in exchange for a substantial discount to the next order;
  • if instead, you don't have any kind of information, then I have to try to capture their attention based on the time period of sending by conveying information related to vacations, for example. I capture their attention and ask them for a small "commitment": in this case a feedback about their last experience with us, in exchange for a substantial discount.

What interests us most is that customers interact with us asking them a small commitment, that shows us there is still an interest. Once I get the click, I'm ready to tag him as partially active and push it into the flow. 

It starts when you put tag "inactive" a contact, and: 

send an email for feedback request

IF – It's not opened and/or clicked

THEN – Enter in a reminder sequence and qualify it as temporary-lost

ELSE (has clicked) – IF he has completed feedback process

THEN – Apply tag active and remove inactive

ELSE – Enter in a reminder sequence untile qualify it as temporary-lost

Note how are much longer the NO cases instead of affirmative one.

Click here to download a marketing automation template.