You Ask, I Answer: RFM Analysis for Small Business with Google Sheets and IBM Watson Studio

CC asks, “What’s the most valuable analysis overall for a small business to perform?”

If we define a small business as an enterprise with less than $1 million in sales and fewer than 10 employees, then from an overall business perspective it’s got to be RFM analysis. Nothing else comes close in terms of helping a business understand its customers better and find relatively quick, relatively easy wins to grow revenue. Watch the video for more details. Watch the video for a tutorial on how to perform this analysis.

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In today’s episode cc asks, What is the most valuable of data analysis that a small business can do overall, what’s the most valuable analysis that a small business could perform? Now, if we define a small business as an enterprise with say less than a million in revenue and fewer than 10 employees, then from an overall perspective, you’re talking about like a storefront, a retail shop, a small consulting firm, startup software company, restaurant, the analysis that is make or break for that business.

And I think the most valuable one is our FM analysis.

Our FM stands for recency, frequency, and monetary value.

That’s a way of analyzing your customers to understand what who are the most valuable customers.

Because from that analysis, we can make several determinations about what to do next, to increase value for the business.

Nothing else comes close to me in terms of helping a business understand its customers better, and find those quick wins to grow revenue.

So let’s look at what this looks like because it’s on the is also a technique that I think is easy enough to perform in a spreadsheet.

And obviously, if you have some visualization tools, and some more advanced data analytics, you can use those but you don’t need those in order to be able to do basic RFM analysis.

So let’s let’s put this over here are Femina Alice’s begins by getting all your customer data and you need when was the last time somebody purchased something, how many purchases have they made, and the monetary value of those purchases now because those are three very different metrics.

There are three different scales, recency is probably a date, range, frequency is probably an integer value, number of purchases, and monetary value is going to be a floating point number, right currency.

You will need to scale that you need to to to normalize and scale them.

And the best way to do that is within us within spreadsheet software, it’s easy to do, you can do it obviously and more advanced technology.

But what you want to do is essentially rearrange those values to be consistently on a one to 10 scale.

So you would say with reasons that you What’s the oldest possible date, what’s the newest possible date, and then have recency be a 10 is the most recent and a one is the oldest frequency again, you take that scale that min max and you can use actually use the formula min max in many spreadsheet packages.

To do that same rearranging of most frequent purchases 10 least frequent purchases want and really scale your data back and saying for monetary value, when you’ve done that, you’ll get a spreadsheet that looks like this, your customer ID, you’ll have your recency one to 10 frequency one to 10 and monetary value one to 10, I would suggest adding in a total value column called total value, and just doing a sum across the table.

Now I have these this is dummy data.

So you’ll notice that values are changing because it’s it is simply just randomized numbers.

But now I have let’s go ahead and copy this to a new sheet so that the numbers stop changing.

All right.

Paste Special values only.

Now if I rearrange my table, a little bit Conditional Formatting here just to make it easy to see.

Customer AA two is my most valuable customer, right.

High recency, high frequency, high monetary value.

Let me scroll down here, let me get into the into the service suburbs customer a 770 is my least valuable customer, right low frequency, low risk, low recency or low frequency low monetary value.

Now from this, there’s a couple things that we can do.

Number one, for those customers, which are the most valuable customers, and you may want to break this into core tiles or whatever statistical format you want to just do it in the third’s break the table into thirds.

Your most valuable customers, you’ve got to keep them happy.

So you know, your top 10.

Those are the folks that you would want to invite to like a customer to a focus group or something.

Let’s say you know your restaurant, right, those top 10 customers, you invite them in for a once a year, maybe twice a year, come in for a free meal.

And tell us what you like and don’t like right because you’re here a lot we know you bye name Come on in.

Same is true.

If you are a SAS software company, customer advisory board, those are the people that you invite to your customer advisory board, because they’re the ones giving you all the money for those customers, you will then want to do a bit of segmentation on this.

Who comes in who has comes in a lot, but isn’t worth a lot and rearrange the table and sort that and say okay, well, what can we do to get those people come in more, right or bought or buy more frequently from us as many more email marketing more retargeting ads? for those folks who are not monetary value, but come in a lot? What can we do to get you to buy more those people you might want to run surveys to and say, hey, what, what do you wish we had that we don’t have as as products and services.

Now, again, there are there are straightforward ways of doing that sort of analysis.

And then there are very advanced ways of doing that analysis.

One of my favorite ways to do clustering.

And this is something that you can do in software like IBM Watson studio.

So I’m going to show you a quick example to overwrite that.

And let’s go ahead and click in here.

I want to refine this.

I’ll do a quick visualization on this.

Penn fun, let’s just do a little bit of 3d.

We want our recency, our frequency, our monetary value.

And then for our color, our total value.

Right.

So in this case, the red or the.is, the more valuable it is.

So you can really see there’s some customers here, they’re just in the suburbs right there.

These are the customers that are not great.

If I have customers who are high frequency, high recency but low monetary value, like this person right here in the corner.

That would be somebody’s customer ID on that.

So recency, frequency, high recency, high frequency, low monetary value, this person right here a 94? That’d be somebody I’d reach out to and say, hey, what can we do to get you to buy more? On the other hand, we have somebody who has high monetary value, right? But low frequency they they were in recently, what can we do to get them to buy more so you can we can cluster our data with more sophisticated tools to really pull out segments and market to them.

But that, to me is the most valuable form of analysis we can do as a small business.

And again, you don’t have to use the cool fancy visualization tools, you really can do all this and just a very basic spreadsheet to understand who your customers that are most viable, who are the customers who you could increase their value.

And then who are the customers who it may not be worth your time to focus in on them.

So that analysis is something that every small business owner should be doing.

I would say honestly monthly so that you’re staying on top of your customer base and you’re seeing the changes like are your efforts to bring back in frequent customers? Are they working and getting customers to come back more frequently? So great question, important question.

I hope that you run this analysis for yourself, for your businesses.

And if you have any further column comments, leave them in the comments box below.

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