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Improving the Response Rates of Email Marketing


If you are like most marketing managers the topmost thing in your mind is to generate more revenue with your marketing spend. Perhaps your performance is measured on it. In effect, you are expected to invent the perpetual machine which takes no input but generates infinite output, or so it seems.

Most companies today use emails as the main way to communicate their marketing messages with their customers. Yet the response rates are so low that the gut reaction to poor email responses to your email marketing program is to increase the email frequency to improve the number of responders.

However, this leads the customers to perceive your messages as an irritant if not spam. Even if you are not flagged as a spammer or an irritant there is a strong possibility that the value of your message is diluted, thus creating a long-term loss of brand value. Is there anything that an organization with a modest budget, can do to improve response rates without barraging your customers with unwanted emails?

Fortunately, there is a way to engage your customers without bombarding them with emails and yet improve the response rates of your email marketing.

Let us look at how you can segment your customers with whom you want to communicate.

Broadly speaking there are four different types of customer categories:

  • The persuadable
  • The sure bets
  • The lost causes
  • The “Do not Disturb”

The persuadable are those that are likely to be seeking a product or service and are familiar with your brand and aware of your offerings. These customers are likely to welcome your email because it solves a problem they are trying to solve. Perhaps they are interested in buying a product you are offering and so your email seems to be well-timed. Here the need is met just in time, or there is an untapped desire, unspent disposable income that you can access by sending the right message at the right time to the right person. The persuadable are also the customers who will spend higher if targeted.

The sure bets are those customers who are very familiar with your brand and offering. These customers may buy irrespective of receiving an email/catalog/SMS/coupon. You may potentially waste your money by sending them emails, or better still reduce the profitable revenue generated by your marketing program by offering them coupons. This is preaching to the choir.

The lost causes are those who are never likely to respond to marketing messages as they are either not interested in buying, or they have been won over by your competition. Sending them emails may be fruitless and you are better off trying your message somewhere else.

The Do Not Disturb: The fourth category of customers are those who are likely to be loyal customers but who don’t want to be disturbed by frequent emails. Sending them emails is likely to turn them off. You can lose a good customer due to poor marketing. Generally, these customers feel slighted that you don’t know them and get put off by your marketing emails. This is a risk you can’t afford to take, as it would mean losing a good but infrequent customer who buys a lot whenever they get to your store or website.

The question by now you must be asking is all this is good but how do I segment my customers in these four categories. I will get to the process of effective segmentation later first let us look at the historical and current situation.

Historically experienced marketing managers have developed an intuition based on observing the behavior of their customers.

  • When did they last buy?
  • How frequently do they buy?
  • How much do they buy?

The trade term for this formula or expertise is called RFM (Recency, frequency, and monetary) value. For years this has been a mechanism used to segment customers by these three dimensions and target them with marketing messages. But this technique has been overused. Along with this, the avenues for buying have increased significantly as well. Besides retail stores, there are now e-commerce sites and mobile apps where the buyer can exercise the right to buy. They can buy in their bedroom late at night, in their pajamas, or buy while they are riding a car during their daily commute. So the customer is getting empowered to buy anything, anywhere, anytime.

The advertising influences on a customer are increasing multifold. Google search, ratings and reviews, social media bragging by friends about what a great deal they got are routine. So what in your marketing really worked? What can you attribute the sale to? This is the holy grail of marketing today. My point is that just a three-dimensional analysis of customers doesn’t give enough insight into their buying behavior. Obviously, a better way to analyze customer behavior is needed.

Over the years direct marketing companies have used predictive modeling for creating multiple segments of the customers based on a large number of variables that are likely to influence the buying behavior. Obviously, you couldn’t mail the catalog to all the people in the country as it costs real money to get the catalog in the hands of a customer. Even if it costs $0.50 per catalog to send a 50-page catalog to a customer the numbers quickly add up when you mail the whole population multiple times a year. Hence the need to improve targeting. Marketing managers have developed very deep expertise to increase the return on investment of the marketing dollars. In direct marketing, predictive modeling is used to calculate a purchase propensity score (the probability of purchase multiplied by the amount of money the customer is likely to spend) for each customer. This gives a sense of the success of the campaign before any mailing is done. The use of this technique has not been applied to email marketing mostly due to the cost of modeling and scoring the customers. There is also a notion that it costs very little (at least relatively!) to send an email blast, so I might as well send it to every one of my customers.

Both the cost of the modeling and the almost negligible cost of emailing have kept this approach from being used for email modeling.

However, our experience over the last few years has been quite the opposite. Typically most companies are happy if they get a 1%- 2.5% rate of response to their email marketing. But using the approach I am about to outline, we have experienced response rates in the 12-15% range. Initially, when we reviewed the numbers we didn’t buy them, but when the rates continued to keep coming up again and again it became a conviction that we are on to something.

In the next few posts, I will attempt to articulate this approach and look forward to your feedback. What we will attempt to learn together are the issues involved and how to overcome these to attain the marketing nirvana of “sending the right message to the right customer at the right time based on their moods, likings, buying stage, and buying behavior”. Stay tuned.

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