Earn more money with DB marketing - Part 2
Posted 30th January 2009 at 03:45 PM by awesometbn
Tags cross-selling, kotler, upsell, when to stop
To decide which customers should receive a particular offer
Okay, so here you are sitting pretty on top of a big database of customers and their detailed history of interactions with the various departments at your company. What do you do now? What next? The answer is you never stop looking for ways to delight the customer, by sending them more specific and targeted offers for your product or service. The main idea is to reuse your existing data by selling, up-selling, and cross-selling.
A good example is amazon.com and their method of linking your current product view with relevant data about other customer purchases, recommended product ideas, and product reviews. Another example is prompting the customer before they can see the checkout screen summary to consider other items they might want to add on. Do these promotions apply to everyone? Even if it does, it won't be the same product push, it will be a closely related item that fits the preferences and buying habits of the customer. Understand this is more than just figuring out the database records for a particular customer (name and address, etc.), it really becomes an analysis of your products themselves and observations about all of the transactions. If you see patterns, there might be a way to take advantage of those patterns. Kind of like hooking into a stream of flowing money.
The other chief idea here is to use business rules for all of your product offers. What this means, is to tailor your descriptions and discounts based on the timing of your customer response. A better way to say this is by calling it business logic (like programming, or an algorithm). If you send out a flyer about a new product to a list of 100, and get back 30 replies the first two weeks, send the flyer to the remaining people on the list with a new second offer. This time you get back 40 replies. If you are convinced that you need to chase after the complete list of 100, and have already qualified them as ideal customers, try using a personal touch with thank you notes, or freebie incentives to win their business.
Kotler's example was gathering enough data about your customers to know when it is time to stop marketing to them. Don't get too caught up in the fever of selling that you forget your business plan and meeting the financial goals of your company. Use your database to carefully decide who would be the most profitable customer if they responded, and who you need to leave alone for the time being.
Let's hear some feedback from you, the reader. How do you decide which customers should receive a particular offer? Your comments begin below. Part three will discuss how to use your database to deepen customer loyalty.
Okay, so here you are sitting pretty on top of a big database of customers and their detailed history of interactions with the various departments at your company. What do you do now? What next? The answer is you never stop looking for ways to delight the customer, by sending them more specific and targeted offers for your product or service. The main idea is to reuse your existing data by selling, up-selling, and cross-selling.
A good example is amazon.com and their method of linking your current product view with relevant data about other customer purchases, recommended product ideas, and product reviews. Another example is prompting the customer before they can see the checkout screen summary to consider other items they might want to add on. Do these promotions apply to everyone? Even if it does, it won't be the same product push, it will be a closely related item that fits the preferences and buying habits of the customer. Understand this is more than just figuring out the database records for a particular customer (name and address, etc.), it really becomes an analysis of your products themselves and observations about all of the transactions. If you see patterns, there might be a way to take advantage of those patterns. Kind of like hooking into a stream of flowing money.
The other chief idea here is to use business rules for all of your product offers. What this means, is to tailor your descriptions and discounts based on the timing of your customer response. A better way to say this is by calling it business logic (like programming, or an algorithm). If you send out a flyer about a new product to a list of 100, and get back 30 replies the first two weeks, send the flyer to the remaining people on the list with a new second offer. This time you get back 40 replies. If you are convinced that you need to chase after the complete list of 100, and have already qualified them as ideal customers, try using a personal touch with thank you notes, or freebie incentives to win their business.
Kotler's example was gathering enough data about your customers to know when it is time to stop marketing to them. Don't get too caught up in the fever of selling that you forget your business plan and meeting the financial goals of your company. Use your database to carefully decide who would be the most profitable customer if they responded, and who you need to leave alone for the time being.
Let's hear some feedback from you, the reader. How do you decide which customers should receive a particular offer? Your comments begin below. Part three will discuss how to use your database to deepen customer loyalty.
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