Homebrew Digital Analytics: Making the Most of Your Transaction Data.
“It is a truth universally acknowledged that a digital campaign in possession of a meagre budget, must be in want of some good data” – Jane, Digital Marketing Manager.
A common dilemma faced by digital marketers is that while most have access to transaction data (like sales history and eDM campaign data), it is difficult to get a hold of the really interesting morsels like customer demographics and interests, at an actionable per customer level. Personally Identifiable Information (PII) is not only difficult and expensive to gather, they are subject to a range of privacy rules. All these unfortunately add up to more often than not, blanket campaigns that overshoot key customer segments you know are the ones you really want to target. Without breaking the bank, what can a digital marketer do with the available data on hand?
The answer to the above is ‘quite a bit’.
Transaction data and platforms hold a treasure trove of insights if you are willing to spend the time to pry it open. Below are just some of the methods marketers should consider using to create actionable customer segments.
Recency, Frequency, Monetary Value (RFM) Models.
For retailers online and offline, this is almost a must have. Mapping your customer’s purchase patterns in terms of how recent it was, their frequency and the average size of each transaction is a way to determine and segment the most valuable from the rest. Such data also happens to exist in abundance – although you might need to do a bit of wrangling and reformatting from whatever platform you are pulling it out of.
In terms of analysis, you can make it as simple or as fancy as you like – from filtering the data in a spreadsheet to piping it into a fancy data visualisation application against all 3 dimensions. Breaking each dimension up into data range blocks of 20% (or quintiles) is one useful way to analyse the data. With it, you can not only cross reference which customers are more valuable than others but also the actions you need to take for each. Below is an (artificial) example with 'Recency' of transactions shown in different colours.
Geo-Targeting Customer Purchase Patterns
An oldie but a goodie is using shipping or billing address to find pockets of customers more amenable to your offers. Tools like Tableau are really great at automatically helping you to visualise the copious amount of data - see below (artificial) example. You can then use it to optimise campaigns, by for example bidding higher on ad locations you know will be more likely convert.
Enriching Your Mailchimp Subscriber Data
This one involves a bit of tinkering but the idea is to cross reference your Mailchimp subscriber data with website visitor profile data available on Google Analytics – without violating any of Google’s Personally Identifiable Information (PII) rules. The end result being you get to collect and profile subscribers that click on any eDM campaign linked to a page on your website.
Check out this great Luna Metrics blog for a step by step guide - http://www.lunametrics.com/blog/2013/06/17/email-tracking-google-analytics/
Customer Lifetime Value (CLTV)
Ever wondered whether all those ad dollars you are pouring into digital campaigns are worth it? That’s what CLTV is all about – determining how much a customer is worth over their lifetime. Typically, this involves using some assumptions around
- Churn or retention rates of customers
- A discount applied to future revenue dollars
- Gross Margins or Average Order Value
- The horizon period over which the model is applied
It can also be as simple or as complicated as you want. Check out this great infographic from KISS Metrics for how to calculate it - https://blog.kissmetrics.com/how-to-calculate-lifetime-value/?wide=1
If you have suggestions on other methods, tips or tricks, feel free to share and add it to the above list.
Christopher Tia is the eCommerce and Online Analytics Manager at Lowes. All views expressed are his own.
Connect on LinkedIn: au.linkedin.com/in/christophertia