What separates winners from the rest?
Three customer analytics well worth doing right now.
Over the years, Ive collected benchmarks (and the research to prove them) that separate winning companies from the rest. Some of these benchmarks are downright esoteric (SOV/SOM ratios ) some are impossible to achieve for a small business (market dominance through sales points) but many are applicable to our daily business.
Recently, e-Marketer published yet another one that is highly interesting: Consumer Analysis
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The most obvious difference
- Most winning companies use some form of analysis.
- Only 2% of leaders dont use any kind of predictive analysis. Thats over 8:1!
Raising your small business marketing game
If your company is not using consumer insight analytics this is one way to raise your game. Which one will give you the most bang for the buck and be easy to implement as well?
Here are three key questions to consider:
- What information do you have available?
- For how long have you had it? (Trending)
- In which software or how does it export?
Three marketing tactics to effectively address, retain, capture and grow your small business customer base
1. Response Modeling 37% more of the leaders use it.
The advantages of a correctly developed response model are enormous. By zeroing in on just those consumers most likely to respond to a product offer, the marketer is able to specifically craft the mailing to each consumer.
2. Customer Lifetime Value modeling 35% more than non-leaders
Customer lifetime value is a critical metric for any business. Those that are able to measure and maximize the lifetime value of their customers have a distinct competitive advantage over those who do not.
3. Segmentation 33% advantage
The process of defining and subdividing a large homogenous market into clearly identifiable segments having similar needs, wants, or demand characteristics. Its objective is to design a marketing mix that precisely matches the expectations of customers in the targeted segment.
I. A Closer Look at Response Modeling.
At its core, response modeling is amazingly simple: you do something, track its results and plot out the cost data to identify whatever returns the most for the least amount of resources.
Vital information required:
- Program costs (how much did you spend doing what)
- Sales data tied to those responses
Here’s an example of a simplistic response model:
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Results & ROI
As a starting point lets say you did 10 things and spent about $1,000 or so in each action….
- How many responses were generated? Responses could be anything: a call, a click
- Divide those responses by their cost and see the Cost/Response.
- But thats only a partial picture. Look at the actual sales generated by each action. For example, #10 could be a Groupon sale, #3 could be a direct mail piece.
- Put Total Sales in a column, divide by responses and you have your Average Sales.
- Divide Total Sales into Total Cost you get your ROI.
In this case, #9 is a clear winner: $1,200 invested returned $1,520 in sales. #5 is a clear loser: $1,200 invested returned only $550 in sales.
The next step- Strategy: is there anything you can do to improve your return?
Take #10. The average for all sales is $427. Taking those 5 sales and taking them to the average, would yield $427 x 5 = $2,135 for an investment of $1,300 and an ROI of 64%
These analytics help track results AND aid strategic direction. How can you miss?
Next week well look at Customer Lifetime Value modeling and Segmentation .
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