Direct Marketing Automation Guide for Beginners. Increase online store revenue by implementing better automation flows. Filipp Volnov

METRICS ARE USED IN DIRECT MARKETING

      Companies measure the effectiveness of direct marketing through a number of metrics, most of which are related, well, to money. With a few exceptions, of course: some companies can’t always track the connection “down to the money’: for example, when sales data “lives separately’ in other data silo from emails (more often than not, that can be fixed!), or sales are made in a location not related to the brand in any way.

      For example, a FMCG manufacturer Kimberly Clark sends email campaigns to a base of subscribers who then buy Huggies diapers at Costco or Amazon. In this case, it is possible to trace the connection between campaigns and sales only by finding a correlation in bursts of marketing activity and the background of sales.

      In most cases, the businesses I’ve had the opportunity to work with have used the following metrics:

      – Share of direct channel revenue in total year revenue

      – Order conversion compared to the control group

      – Retention rate, or customer retention rate

      – Absolute revenue of the channel for the period

      – Average check per customer for the period

      – Increase in active customer base

      If a business hasn’t tracked marketing metrics before, I recommend choosing at least one metric to start with that is closer and clearer to the business and is easy to implement. Let’s break down each metric in more detail.

      Share of direct channel revenue in total revenue

      Formula

      Revenue share from Direct Channels = (Direct channels revenue / Total Revenue) x 100%

      Benefit

      Shows how much money, relative to other channels, direct customer interaction channels – SMS, email, mobile, web and messengers – bring to the business.

      For example, store 12 STOREEZ set a goal to increase the share of revenue from direct communications. The point is that customers began to read newsletters less often and it was necessary to bring back the interaction that was conducted through five channels: email, mobile push, SMS, web push, OSMI Cards push notifications. The store audited the mailings, began working to improve the effectiveness of each with AB tests, and conducted a survey of inactive subscribers. The results: the share of revenue from direct communications grew to 30.43%. That is, almost a third of revenue came from customers who were already in the base and didn’t need to spend budget to attract them. If a business has only an email channel, consider the share of the email channel in total revenue.

      Risks in tracking the metric

      The share of revenue from direct channels grows, but the total revenue does not. This is likely to happen if, for example, direct channels “ate” the revenue of other channels. That is, the revenue of direct channels has grown not because they are efficient, but because the revenue from other channels has been redistributed to direct channels.

      To know exactly what caused the metric to rise, I suggest tracking the share of direct channel revenue in conjunction with total revenue and using a test with a control group. In marketing, control groups are used to measure the impact of a specific campaign or customer journey. Specifically, control groups are the customers you are targeting with a particular campaign who will not receive that campaign.

      Conversion to order

      Formula

      Order conversion rate = (Total orders number / Total number of visits) x 100%

      Benefit

      Shows how marketing affects the number of orders from a particular channel.

      Online supermarket Arbuz.kz implemented personalization and marketing automation. In order to track how it affects sales, it measured the conversion rate from going from an email to an order, and compared email channel performance YoY.

      Result: conversion to order from email increased by 23%. So, marketing automation has improved conversion rates. The next step is to calculate how much money it brings in.

      Risks in tracking the metric

      If you don’t compare it with a control group, there is a risk of drawing the wrong conclusions. A fairly common situation is when a customer comes from context ads to a website, creates an account, receives a welcome email flow, follows the link and makes a purchase. Analytics tools show that the customer made a purchase attributed to the welcome email because it is the last significant communication channel.

      Revenue from such a purchase will be at the share of the email channel. If a customer has left a mail on the website, most likely they are interested in buying something. We need to find out whether the welcome letter plays a decisive role and to which channel it is fairer to attribute the purchase – to mailings or contextual advertising. This can only be done by conducting a test with a control group. As part of the test, some customers will receive a welcome letter after registration, while others will not. If the background of sales in these groups is different, it means that the welcome letter really influences the decision to buy. In this case, it is logical to attribute the purchase to the welcome email flow.