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Digital Segmentation by consumer channel preference

This is yet another way to segment consumers and is based on the premise that is inherent in a multi-channel environment. While it may not work across all kinds of needs, it helps determine which channel is more likely to be used, especially when a business needs to continuously invest in multiple channels.

The fundamental premise of this is what is known as a "Technology Acceptance Model". This has several elements. When confronted with the option of using a channel, the consumer has a perception about the usefulness of the channel (PU). This is layered with the perception of the ease of use of the channel (PEOU). If the channel is perceived to be useful but not easy to use, the likelihood of its being used reduces. Similarly, although the channel is perceived to be easy to use, if its usefulness is questionable, the channel is unlikely to be actually used.

It is only when both PE and PEOU get high scores in the minds of the consumer that the intention to actually use that particular channel is validated - and confirmed upon actual usage. The preferred channel in the model is therefore the channel with the highest intention to use.

The model can be explained with a simple example. Let us assume a consumer wants to buy (for the first time in her life) a life insurance policy. Her channel set is caused by her experience with channels in former service settings or as a result of external factors like social influence of her family or marketing campaigns. Her channel set might consist of for instance three channels: telephone, Internet, branch office. She might use the Internet to gather information and might go to a branch office for closing the contract. After evaluation of her experience, her (presumably quite subconscious) expectations will be confirmed or disconfirmed. This will have impact on the preferences of the three channels within the channel set, which will determine the channel choice during the next purchase of insurance.

Studies have revealed some interesting insights into how men and women rate channel preferences.

• In evaluating channels women are more outcome-oriented; men are more convenience-oriented
• The evaluation of the channels shows that men are more positive about the Internet; women on the other hand are more positive about the mobile Internet.
• Although for all groups the Internet is the most popular channel, the face-to-face channel is most popular among the lowest educated, the telephone among the highest educated.
• The middle educated score highest on the satisfaction scores with the experiment; the highest educated score lowest. The higher educated might be more critical in general or they might be more critical because they have more experience with buying travel insurance.

So there you have it! A usable segmentation model based on usage of various channels by consumer preference. Try it out the next time you are faced with a budget allocation conflict across multiple channels. It will surely help you identify the channel that will give you the best return on investment!

Digital Marketing Segmentation on the basis of purchase behavior

Purchase behavior in the online world is a very different entity when compared with the traditional world. With the rise in social and digital media platforms, consumers are constantly evolving and changing the ways in which they research and purchase online. New shopping paths are emerging depending on behavior, device, location, and intent. It’s not just what consumers do that is important; it is also how, when and why they do it.

Consumers are increasingly distracted, but smarter. Marketers don't need to lag behind! Digital marketers need to take a hard look into the data trail that consumers leave behind. Analysis of this behavior can provide actionable insight into how consumers arrive at their purchase decision.

One of the chief ways in which this can be done is in terms of the purchase behavior of the consumers. In order to implement some key questions need to be answered. They include, what does your consumer buy online? How frequently? Each time they buy, how much do they spend? Do they buy products only from a particular category? Or do they often buy from a variety of categories? The answers to these questions will enable you to do what is commonly known as a FRAC Analysis in the digital marketing world.

Simply put, FRAC Analysis involves:

• Frequency = the number of transactions the customer has made in a fixed time period
• Recency = interval between the times when the latest consuming activity happened and the present
• Amount = monetary value of each purchase action
• Category = types of product purchased, singly or together

This is an excellent way to segment your existing customer base and slot them into buckets. Some buckets will respond better to offers and promotions than others, so your email marketing or App marketing efforts can be concentrated on them for best results. Not only that, over time, the FRAC Analysis can also begin to help you predict how the customer lifetime value changes over time and to spot trends that help you maintain/retain the hold you have on your most profitable customers.