I was listening to a webinar earlier this week when one of the participants was touting the use of "big data" to determine what a company should be offering its customers based on weather.
With all of the talk about "big data," don't forget what your can do with all the "small data" you already have on hand.
When I began advertising NyQuil 20+ years ago, we used public school absentee data from the Center for Disease Control (CDC) to predict flu outbreaks and drop coupons via free-standing-inserts in markets where absenteeism was high.
When working on Wondra Hand and Body lotion for Procter & Gamble, I was able to use Nielsen and weather data to show a strong negative correlation between body lotion sales and relative humidity. More people use body lotion when and where the air is dry -- go figure.
Using data to improve the effectiveness of your marketing is not rocket science, it's common sense.
Most recently as the director of operations and marketing for an Irish wastewater, water reuse and odor/VOC control company, I was able to show a 96.5% positive correlation between new housing permits and sales of the company's residential wastewater treatment systems based on 12 years of monthly data.
While it didn't save my job in the recession, it did show how strongly the U.S. housing market affected our business.
There are myriad ways to use data to improve your customer experience and your marketing.
Let me know if I can help you use your data to answer a question or solve a business problem.
Great presentation by Gary Cokins, founder of Analytics Based Performance Management.
Gary suggests that the Pareto principle (the 80:20 rule) applies to cost to serve the same as it applies to revenue and profitability and that by eliminating the 20% of customers that account for 80% of customer service expense you can dramatically increase your firm's profitability.
The key is to know the lifetime value of your customer and the cost to serve them.
Following are 11 ways to increase profitability:
- Be aware of the service cost for each customer and reduce it. What can you do to making buying from you more simple? This will save the customer time and save you money. Customers crave simplicity.
- Establish a surcharge for, or reprice, expensive cost-to-serve activities. If it costs more to give the customer what they want, you need to educate the customer of this and charge them for the extra expense you are incurring.
- Reduce services. Customers today are very adept at searching your website, and others, for answers to their questions. Provide the information they're looking for with a content-based marketing effort. Answer any question you've ever received from a customer in a blog post. It will help your prospects and customers as well as your search engine optimization efforts.
- Introduce new product and service lines based on your customers' needs and wants. Empower your employees to help identify what your customers' needs and wants are.
- Raise prices. The 2012 American Express Global Customer Service Barometer found that customers would spend 13% more with companies that provide great service.
- Abandon unprofitable or less lucrative products, services or customers. Don't do this before you've tried charging more.
- Improve processes to drive up service line or product profitability. Start by having an accurate customer relationship management database that every customer-facing employee has access to.
- Offer the customer profit-positive service level options at varying prices. Zappos is known for providing a consistently outstanding customer experience and next day delivery -- not everyday low prices.
- Increase activities that a customer shows a preference for. Fine dining establishments have been charging premium prices for "chef's tables" for years.
- Up-sell and cross-sell the customer's purchase mix toward richer, higher-margin products and service lines. Leverage data to know what to offer your customer next to fulfill their needs.
- Discount to gain more volume, or greater lifetime value, with low "cost-to-serve" customers. I receive monthly shipments of dog food and cereal at a 10% discount. I save time and money and the companies selling me their products now have an annuity stream.
When I worked on the Bounce Fabric Softener account, I created bi-monthly Nielsen analyses for our client.
Nielsen provided market share, pricing and distribution information for food, drug and mass merchandisers for Bounce, and each of its competitors.
Fortunately, Procter & Gamble had been subscribing to this data for a number of years before I began working on the account so I had plenty of data with which to run single and multi-variable regression analysis.
With multiple-variable regression analysis, I was able to show when Bounce was priced no more than 30% higher than generics, it gained share.
When the price difference became greater than 30% it lost share.
By optimizing Bounce's price, we were able to compete against generics, without losing share, while maximizing revenue.
Do you have sufficient data to know the price elasticity of your brand?
I was surprised at the findings revealed by Dr. Christine Moorman, director of The CMO Survey.
The results of the most recent survey showed a decline in the number of projects in which companies use marketing analytics that are available and/or requested. According to CMOs surveyed, they report a 30% usage rate. This number is down from 37% a year ago.
"Big data" has been the rage for the past few years. CMOs recently reported that the percent of their marketing budgets devoted to big data will increase from 6% to 10%.
So while more is being spent on, and written about, "big data," less of it is being used.
Recently in a LinkedIn discussion group, "Chief Marketing Officer Network," someone posed the question, "Do you think 'big data' actually baffles most marketers?"
I think Dr. Brian Monger hit the nail on the head when he said, "I think 'big data' baffles a lot of folks. It's why you not only need a good analyst to do the numbers, but an even better insight person to work with the analyst and the numbers."
I know there are several graduate programs in business analytics; however, I don't know that these programs teach people how to look for "insights" in analytics.
I had the pleasure of working on Procter & Gamble, Richardson-Vicks and Warner Lambert business early in my career. We received Nielsen data every two months on sales, market share and distribution by channel (food, drug and mass merchandiser).
There were also quarterly spending reports from MRI and other sources.
While some might not call this "big data," my understanding is that it came from SAS and there was certainly enough to run single and multi-variable regression analyses that enabled me to determine:
- The price differential at which Bounce Fabric Softener would lose share to generics.
- The negative correlation between Wondra Hand & Body Lotion's brand development index (BDI) and average relative humidity.
- The positive correlation between advertising spending and Rolaids' sales.
Subsequently I worked on Wachovia Bank for five years which fielded quarterly research to determine top-of-mind awareness, market share and switching preference. The availability of this data enabled me to show the positive correlation between switching preference and emotional versus rational advertising.
Most recently I was the director of marketing for the U.S. subsidiary of an Ireland-based manufacturer of wastewater treatment systems. When I joined the firm, there was a great deal of concern over the decline in sales since 2009.
By looking at sales and housing permits by month for the past 12 years, I was able to show a 95.6% positive correlation between housing permits and sales of their peat-fiber biofilter.
While this didn't increase sales, it certainly explained the decline. This year, coincidently, sales are back up, along with new home permits.
With all of the data collected and available today, the ability to show positive and negative correlations, and to model future outcomes, is infinite.
However, you need to know what to look for. What insights do you want to get?
I recommend creating hypotheses about what is, or is not, driving customer behavior, sales, market share, or what ever else you want to know about your business.
Once you have created the hypotheses, you can then perform the data analysis to prove or disprove your hypotheses. The results of this analysis are insights.
What questions can data answer for you?
Let me know if I can serve as an insight person to help you find those answers.