Far too many companies that I consult with sit on loads of good customer data…and do nothing with it. It’s truly amazing, because in that data is a gold mine of insight.
Insight that can:
- Increase customer loyalty
- Unlock hidden profitability
- Reduce client churn
Are you sitting on loads of data that you aren’t using? Would you like to learn how you can use it? Here are the ten most common ways, with some practical advice on how to use each.
1. Basket Analysis
Sometimes called “affinity analysis,” this looks at the items that a customer bought, which could help brick-and-motar stores improve their layouts or online companies like Amazon recommend related products. The “basket” refers to what shoppers use when they are shopping.
It’s based on the assumption that you can predict future customer behavior by past performance, including purchases and preferences. And it’s not just grocery stores that can use this data. Here are a few ways it can be applied in various industries:
- Evaluating use of credit cards. (especially important for online ecommerce). Typically, professionals mine credit card data to find patterns that might suggest fraud, but the data is also used to tailor cards around a variation of credit limits, terms and interest rates…and even collect debt.
- Evaluating patterns of telephone use. For instance, you could discover customers who adopt all of the latest services and features your phone company offers…suggesting they will need something new to stick around…and then offer them an incentive to stay another year.
- Identifying fraud insurance claims. Through the mining of historical information, insurance companies can spot claims with a high percentage of recovering money lost through fraud and develop rules to help them flag future fraudulent claims.
And all the products don’t have to be purchased at the same time. Most customer analytic tools can observe purchases over time, thus helping you spot trends or opportunities that you can test for future promotions.
Take away: Look at your purchase data with an eye for patterns. Do you see people who buy item X also buy Y? Which item did they buy first? Why? Could you encourage people to buy X,Y and Z, thus boosting point-of-purchase sales?
2. Sales Forecasting
This looks at when customers bought, and tries to predict when they will buy again. You could use this type of analysis to determine a strategy of planned obsolescence or figure out complimentary products to sell.
This also looks at the number of customers in your market and predicts how many will actually buy. For example, imagine if you have a coffee shop in Seattle. Here are questions you might ask:
- How many people/households/businesses within a mile of your store will buy your coffee?
- How many competitors are in that mile?
- How many people/households/businesses in 5 miles?
- How many competitors in those 5 miles?
Take away: When it comes to forecasting sales, create three cash flow projections: realistic, optimistic and pessimistic. This way you can plan to have the right amount of capital on hand to endure the worst situation possible if sales don’t go as planned.
3. Database Marketing
By examining customer purchasing patterns and looking at the demographics and psychographics of customers to build profiles, you can create products that will sell themselves.
Of course for a marketer to get any value out of a database, it must continue to grow and evolve. You feed database information from sales, surveys, subscriptions and questionnaires. And then you target customers based upon this intelligence.
Take away: Database marketing begins with collecting information. For example, if you owned a coffee shop, your database might consist of these things:
- Purchase records stored via a club card that you offer via incentives like 5% off purchases or accumulation of points.
- Contests you run to collect additional information about where people live.
- Email newsletter you use to update customers weekly, but also to send out surveys in which you collect additional information concerning new products and promotions.
- Twitter account that doubles as a flash promotion tool and customer service hub where you listen to the good and bad of what your followers are saying…and then respond.
As you collect this data, start to look for opportunities like best days to run a discount promotion. Ask yourself: Who are your local customers and how you can turn these customers in advocates for your store?
4. Merchandise Planning
This is helpful for offline or online companies. For the offline, a company looking to grow by adding stores can evaluate the amount of merchandise they will need by looking at the exact layout of a current store. For an online business, merchandise planning can help you determine stocking options and inventory warehousing.
The right approach will lead to answers that can help you decide what to do with:
- Inventory getting old – Merchandising planning can be as easy as updating a PDF white paper to be current or stocking up-to-date accessories for products.
- Selecting product – Mining your database will help you determine which products customers want, which should include intelligence on your competitors merchandise.
- Balancing your stock – Database mining can also help you determine the right amount of stock…not too much or too little…throughout the year and buying seasons.
- Pricing – Database mining can also help you determine the best price for your products as you uncover customer sensitivity.
Take away: Ignoring this database strategy can lead to poor performance in terms of production and customer service/experience. If you can’t handle typical runs on a product, in-store expectations aren’t met or your price doesn’t match the market, customers will jump ship and go to your competitors.
5. Card Marketing
If your business involves issuing credit cards, you can collect the information from usage, identify customer segments and then based on information on these segments build programs that improve retention, boost acquisition, target products to develop and design prices.
A great example of this occurred when the UN decided to issue a Visa credit card to people who traveled overseas frequently. The agency marketers segmented their database into wealthy travelers—30,000 people in high-income households.
The agency marketers used direct mail for their appeals and generated a 3% response. That may sound small, but it actually exceeded industry standards. Large financial institutions typically see 0.5% response rates. That’s how effective databases can be when marketing cards.
Take away: Of course there are costs built into issuing credit cards that most companies can’t absorb, but if you can…do it. Analyzing customer buying patterns based on their credit card habits will give you insights into behavior that can lead to promotions and programs that will result in higher revenues and better customer loyalty.
6. Call Detail Record Analysis
If your company depends upon telecommunications, then you can mine that incoming data to see use patterns, build customer profiles from these patterns and then construct a tiered pricing structure to maximize profit. Or you could build promotions that reflect your data.
A China mobile operator with about 600,000 customers wanted to analyze their data to create offerings to fend off competition. The first thing the project team behind collecting and analyzing the data did was create an index to describe caller behavior. That index then clustered the callers into 15 segments based on elements like this:
- Minutes Of Usage per user on the average
- Local call percentage
- Long distance call percentage
- IP call percentage
- Roam percentage
- Idle period local call percentage
- Idle period long distance call percentage
- Idle period roam call percentage
From that data the marketing department then created strategies directed at each segment, namely improving customer satisfaction, delivering quality SMS service for another group and encouraging another group to use more minutes.
Take away: Whether it’s based upon mobile user data or customer service calls, dive into the data available in call detail records to look for ways to improve current service, promotion opportunities or ways to shorten time on call.
7. Customer Loyalty
In a world where price wars occur, you will get customers jumping ship every time a competitor offers lower prices. You can use data mining to help minimize this churn, especially with social media.
Spigit uses different data mining techniques from your social media audience to help you acquire and retain more customers. Their programs include:
- Employee innovation – A tool used to ask employees for their ideas on how to improve customer engagement, product development and future growth. Who says data mining is always customer-centric?
- Facebook – Through a technique called “customer cluster” Spigit uses data from your audience on Facebook to generate ideas for improving your brand, satisfying more customers and increasing loyalty.
- FaceOff – This app acts like a place where people can create possible ideas on which to vote. For example, someone might suggest “create the in-flight social network” v. “make a clear view floor so you can see what’s below you when you fly.” Then people are shown these ideas and vote. Naturally this allows a company to find ideas that are coming from customers…and then being voted on by people who might be interested in the final idea.
Take away: Focusing on numbers like Lifetime Customer Value when mining data can help you improve your acquisition costs, but it can also help you identify reasons why customers bail. This is where a combination of tactics may come in handy because your data will probably only tell you where they are falling off. You’ll have to pull some surveys and questionnaires to build a case on why.
8. Market Segmentation
One of the best uses of data mining is to segment your customers. And it’s pretty simple. From your data you can break down your market into meaningful segments like age, income, occupation or gender. And this works whether you are running email marketing campaigns or SEO strategies.
Segmentation can also help you understand your competition. This insight alone will help you identify that the usual suspects are not the only ones targeting the same customer money as you are.
This is especially important, because when I ask most clients who their competitors are they give me a list of people. I then hand them back a bigger list. Most businesses need to expand their circle of competitors out two or three times if they plan on competing effectively. Data mining will help you do that.
Take away: Segmenting your database can improve your conversion rates as you focus your promotions on a tight, highly-interested market. And it can also help you understand who your competitors are in each of those segments, allowing you to customize products and promotions that satisfy the needs of that audience in a way a generic, broad promotion never will.
9. Product Production
Data mining is also perfect for creating custom products designed for market segments. In fact, you can predict which features users may want…although truly innovative products are not created from giving customers what they want.
Rather truly innovative products are created when you look at the data from your customers and spot holes customers are demanding be filled. When it comes to creating that product, these are the elements that must be baked into the product.
- Fulfill an obvious need
- Offer something utterly unique
- Set to enter the market with a unique name
- Attractive design
- Serves a broad market
- Can be sold in generations
- Create an impulse-purchase price
- Cost to make is low enough to make a profit
Take away: The most innovative companies never start with a product. They start with a pain point they’ve uncovered from mining data…and then build a minimum viable product that will solve that problem in a way the customer never imagined. Do this and you will easily be ahead of 90% of your competitors.
Finally, database mining will allow you to predict how many people will actually cash in on the warranty you’ve set up. This is also true for guarantees.
For example, I tested the pulling power that a guarantee has for improving sales on a test I ran on QuickSprout. But before I ran the test I had to analyze data to see how many people would actually return the product I was selling. I looked at previous data on these two sets:
- Net sales
- Settlements made within the defined guarantee
I gathered those two numbers over several different sales sets to predict how many people would cash in on the guarantee…and then adjusted the guarantee amount so as not to lose money when people returned product.
These calculations are typically more robust for large corporations, but for smaller shops you don’t need anything more complicated than that.
Takeaway: One of best ways to creating a successful guarantee is to look at the data of past guarantees, sales and profits. Doing so may lead you to offer a 110% money-back guarantee to get an edge over competition.
The more data you collect from customers…the more value you can deliver to them. And the more value you can deliver to them…the more revenue you can generate.
Data mining is what will help you do that. So, if you are sitting on loads of customer data and not doing anything with it…I want to encourage you to make a plan to start diving into it this week. Do it yourself or hire someone else…whatever it takes. Your bottom line will thank you.
So, how has data mining helped you with your business?
About the Author: Neil Patel is the cofounder of Neil Patel Digital.