TechCities 2015 – Analytics in Digital Marketing Panel Discussion

Posted by: liftpoint | On April 8, 2015

On Friday, March 27th, Mark Price, manager partner of LiftPoint Consulting, participated in a panel discussion on Analytics in Digital Marketing at the TechCities 2015 conference at the Carlson School of Management, University of Minnesota.   Other panel members included Dave Scamehorn, VP of Marketing Analytics, OLSON and Lizzy Wilkins, Senior Data Scientist, Nina Hale, Inc.TechCities2015  The panel was moderated by Chris Erickson and Shraddha Sonawane, M.S. in Business Analytics students at the University of Minnesota.

The panel members discussed topics from challenges they face as digital data analytics consultants to difference analytics for B2C and B2B clients.

Key take-aways from the discussion were:

  • Marketers are working harder than ever and are compensated on relatively short-term revenue or profit goals.   Data analytics projects need to align to marketing executive’s metrics (particularly how they are compensated) to gain buy-in.
  • There are many different tools available for digital marketing data analytics from Google Analytics to Tableau.   The panel members warned against tools that promise easy stitching of data from multiple sources for use by users who are data novices.  Often combining data is more complex than it looks — you need to understand the different data sources in a detailed way to avoid making mistakes.
  • When asked about Big Data, panel members agreed that everyone has a different definition of Big Data.   They discussed the opportunities with large amounts of data in retail, “Internet of Things” data from devices such as vehicles and use of weather data to align to consumers changes in behavior due to weather events.
  • The moderators asked about the differences in Digital Marketing and Traditional Marketing.   The panel members each gave their perspective on how different segments will always respond to different channels but that these digital and traditional channels are merging in many ways. Examples of the merging of traditional media and digital are seen in the the increase in on-line TV viewing (and “binge watching”) as well as the use of digital coupons.
  • The panel members offered some great advice to remember when you are presenting data findings.
    • Focus on the story of the data – what is the data trying to tell you.
    • There is a very human component of data analytics. Make sure that you take into account the political situation into account when presenting data.   Socialize the data ahead of big meetings.   Try to present the data in the context of solutions.

Data Scientists’ Critical Role in Marketing Today

Posted by: liftpoint | On March 6, 2015

Data Scientists - Renainssance Marketer

Data is overwhelming.

Let’s face it.  Most Marketers didn’t learn about using today’s overflow of data in business school. They’re playing a game of catch up.  Marketing leaders are looking to a new marketing role to help them produce actionable data that they can turn into profitable campaigns and customer relationships.   The Data Scientist has emerged as this new critical role in the marketing department.

These hard-to-find people can be your tour guides through the complexities of data. They combine computer science, statistics, math, and business skills with creative problem solving and understandable communication to help you make marketing sense of all that data.

Data Scientist (in its current sense) is a relatively new term, first coined in 2008 by D.L.  Patil and Jeff Hammerbacher, then data analytic leads at LinkedIn and Facebook, respectively.  The Harvard Business Review describes Data Scientists as those who “make discoveries while swimming in data” and who move decision makers “from ad hoc analysis to ongoing conversations with the data.”

The Need

While these professionals are exceedingly valuable, they also are equally rare.  The McKinsey Global Institute predicted “by 2018 the United States could face a shortage of between 140,000 to 190,000 people with deep analytical skills, as well as a shortage of 1.5 million managers and analysts who know how to use the analysis of Big Data to make effective decisions.”

A survey by Robert Half Technology concurs, suggesting that “most companies aren’t maximizing their data collection and don’t have the people in place to do so.”

Marketing and analytics are merging in the C Suite too, with CMO’s increasingly aligned with their CIO’s.  In 2012 only 36% of CMOs said their CIO was a critical partner; by 2014, that percentage grew to 51%.  New titles also are showing up in the C Suite, such as Chief Analytics Officer or Chief Data Scientist.

There is urgency too. Forrester Analytics’ State of Customer Analytics 2014 report concluded that analytics is no longer an option, but a necessity for any organization to compete.  Every organization in every industry needs a senior-level data specialist on their marketing team. Period.

The Skills & Benefits

Data Scientists are today’s renaissance Marketers – with expertise in a diverse collection of areas, each with a positive impact on marketing initiatives.   If you are searching for a Data Scientist to work with your marketing team, below are four areas of expertise for which you should be looking and also a description of why they are important skills for a Marketing Data Scientist.


Programming – Data Scientists know programming languages.  Not just Excel or a Graphical User Interface like SAS or SPSS, but higher level programming languages like Python, R, Java or C++, along with lower level languages that talk directly to the computer, such C and Fortran.

 Marketing benefits: These programming languages can build software or processes for automated recurring initiatives or quick-hit internal analyses. Some of the code also can be “self-learning”.  Over time, the programs collect new data and get more accurate on their own, without human intervention.  Initial results will be the lowest you will get — the results keep improving.  No need for another big project.

Systems – Data Scientists know data warehouse architecture.  They know how to design the systems that house data for most efficient data access and answering business questions.  As data becomes too large to handle on a laptop or desktop computer, warehouse-building skills become important.

Marketing benefit: When architected correctly, these systems permit analysts to quickly find solutions to marketer’s business questions.  No waiting for a week to learn an answer – the right data architecture cuts your wait down to a few hours or less.  That quick analysis turnaround permits marketers to reduce the time they need to take action and increase speed to ROI.


Computational Linear Algebra/Matrix Algebra – Many statistical or machine learning algorithms utilize matrix algebra to produce a solution.  A well-trained Data Scientist will understand this mathematics and not only be able to program existing methods for data analysis, but also manipulate the theoretical foundations to fit the problem being solved. Most variables or fields have missing values when working with Big Data.  Because this is so common, sparsity or sparse matrices might be used to save computational time. Algebraic geometry makes it possible to express geometric representation of data in multiple dimensions.

Marketing benefit:  Life is not linear.  Complex problems require that marketers assess the impact of multiple variables at the same time to help determine the correct answer. The ability of the Data Scientist to capture and assess the impact of all these different factors on your business helps marketers create solutions that will perform well in a complex marketplace.


Applied Statistics – Statistics has evolved with the exponential growth in the volume of data. Assumptions that worked even 20 years ago, do not apply with massive datasets. Today’s Data Scientists use statistics in a more predictive way, to determine the accuracy of an analytic solution against new or holdout data.  Modern techniques would include regularization, online computational statistics and other methods for analyzing and modeling Big Data effectively and efficiently.

Marketing benefit: The growth of Big Data provides marketers with great opportunity and great challenge.  The larger and more varied the data sets, the more complex the potential combinations.  Some will drive success, while others will influence failure.  Out of all those variations, which will work for a particular customer?  This is where applied statistics helps – evaluating individual customer patterns and recommending combinations that will optimize customer behavior – whether retention, increased cross-sell or other behavior.


Domain Expertise – Data Scientists speak business talk.  They can translate all the analytic mumbo jumbo into concepts and theories that non-analytic marketers can understand.  They recognize that a given business question exists inside the context of a given company and industry, and the nuances of those outside influences play on the technical work of solving the business problem.

Marketing benefit:  A Data Scientist with domain knowledge can be a true partner with Marketing.  When analyzing or modeling data, there are many small decisions that must be made to develop an optimal recommendation.  The deeper the business knowledge of Data Scientists, the more likely they will adjust the solution to the unique characteristics of a particular marketplace.  The net results are recommendations that perform better for longer periods of time, as well as a deep and fruitful relationship between Analyst and Marketer.

A Data Scientist addition to your Marketing team creates possibilities for new insights, measureable initiatives, and a less stressful relationship with data.  If you don’t have budget to hire, or can’t find a person with the necessary skill set, small and large consulting firms can give you the benefit of having a Data Scientist on your team, without having to add headcount.

The days of Marketing as a “Creatives Only” fraternity are over.  Today’s Marketers need a data translator to help question, discover, interpret and ultimately succeed, in today’s data world. The era of Data Scientists is here.


Data Guy’s Take on the Starbucks Eggnog Latte Fiasco: Part 2

Posted by: liftpoint | On January 7, 2015

The recent Starbucks Eggnog Latte fiasco is a classic example of that all-important marketing dictum: Mess with Best Customers at your own risk.Best customers will forgive

Yes, Starbucks did apologize and reverse course after first eliminating the Eggnog Latte from their holiday menu.  In my last post, I hypothesized that their mistake was in making a decision based on product revenue trends without considering the preferences of their Best Customers.

Customer-centric retailers, like Starbucks, usually know better.  They avoid such conflicts with their Best Customers.  But even the best can miss something that looks small at the company level but is meaningful for some highly-vocal Best Customers.

So how do we marketers avoid such pitfalls? Here are five strategies that will assure your phone doesn’t ring in the middle of the night because social media has exploded in response to a change in products, pricing or promotions.

1.  Figure out what makes a customer “Best” and who your Best Customers are

Before you can figure out particular preferences of your Best Customers, you first have to know who they are.  As I’ve said before, Best Customers are much more than those who spend the most money. Best Customers interact with the organization on multiple occasions and in multiple ways.

For example, a customer who spends $1,000 once is a good customer.  A BEST Customer spends $100 with you on 10 occasions, likes your product on Facebook, follows your company blog and tweets about your company or product.   These increased touch points breed increased relationship.

2. Start a conversation

Conversation is a critical component of maintaining the Best Customer relationship.  It is the the “keeping the relationship alive” part.  In Starbucks’ case, they support 2-way customer communication across channels such as Twitter, Facebook, YouTube and their own “My Starbucks Idea” site.  You wonder…could Starbucks have floated an idea on Twitter or Facebook:  “How are Eggnog Lattes part of your holiday season?” before deciding whether or not to pull the plug?

How is your organization having a conversation with your Best Customers?  You might start with surveys.  Best Customer advisory councils, preferred reward programs and the ever-growing assortment of social media outlets are all vehicles for 2-way communication.  Finally, Best Customers are more likely to have and use your mobile phone app. Have a conversation with them in as many channels as you can.

3. Analyze data to learn Best Customer product preferences

You will find that your Best Customers purchase many of the same products as the rest of your customer base; however, analysis has shown that Best Customers also purchase certain products that are unique to Best Customers.

By examining Best Customer purchases (“the market basket”), you discover which products with low sales volume have high importance to this segment. Knowing all the types of products that make up the Best Customer market basket helps you maintain the relationship with this critical customer segment.

Since Best Customers know your product line better than any other customers, they also are likely to be the first to create product bundles that a marketer might never have thought about positioning together.  Study those bundles as they change by season and you will find opportunities to grow Best Customers out of the rest of your customer base.

 4. Understand seasonal trends of Best Customers

Best Customers purchase more frequently than other customers (in general), so their transaction data lets you determine seasonal changes in the products purchased.  For other segments, you may be able to see differences by season across the group.  Among Best Customers, you will see the seasonal differences for most of the individual customers in the segment.

Why does this matter? Because this data permits you to speak to customers personally, based on their past transactions.  In the case of the Eggnog Latte (EL), you could identify the specific customers who purchased the EL the prior year.  You could then tailor your communication to those customers, letting them know about the change before the holiday season started, and getting feedback on the importance of the EL to those customers ahead of time.

Big Data, such as weather data, also can be used to understand and predict seasonal purchase trends. For instance, an anticipated thunderstorm will drive sales of windshield wipers, boots and umbrellas.  Giving your Best Customers a special deal during significant weather events can be a smart marketing strategy.

5. Don’t forget the web (and mobile)!

Best Customers purchase from your company through multiple channels – stores, web and mobile.  Make sure to examine purchases across ALL the channels.  If you don’t combine purchases, you will miss patterns that can send your customers “off the deep end” inadvertently.

Likewise, you need to communicate to your Best Customers in the channel they prefer – NOT the one that makes life easier for you.  For example, if you have Best Customers who do not open emails, you need to explore other channels – text, app, even the old standby direct mail, to reach them and get your message through.

Best Customers care for your business.  They will forgive lots of mistakes – broken products, pricing problems, and so on.  But what they will NOT forgive is a failure to listen to them.

As a marketer, you have to be innovating all the time.  And some of those innovations are likely to ruffle a few customer feathers – you just can’t avoid that.  What you want to avoid is frustrating your Best Customers.

Keep innovating, but keep listening at the same time.


For more information about Starbuck’s Eggnog Latte experience:


From USA Today


From Huffington Post


A Data Guy’s Take on Starbuck’s Eggnog Latte Fiasco

Posted by: liftpoint | On December 17, 2014

I love Starbucks! The value of a product equals cTheir product selection, store design and customer service is structured to make people like me feel good. Their loyalty program is rich and I love paying with my mobile phone app.  I also enjoy the chitchat with baristas who willingly create drinks that meet the “high maintenance” needs of customers like me.

Given my love affair with Starbucks and all the data I know they have on their best customers, I was surprised to learn that Starbucks decided to not offer their 20-year, traditional eggnog latte for Holiday Season 2014. With the exception of Pacific Northwest stores (as I understand it), Starbucks chose to replace the eggnog latte with another seasonal coffee drink.

For what is probably a small-volume, seasonal drink with, I imagine, an equally small audience, Starbucks was surprised by the furor that this decision created. Websites were built. Starbuck’s “My Starbucks Idea” forum exploded. Twitter was flooded. Facebook filled with loyal consumers threatening boycotts and demanding “Bring Eggnog Latte Back”.

How could Starbucks have missed the mark so dramatically, especially during the critical holiday season?

I’m obviously not a Starbucks insider, but let me hypothesize from a data-driven perspective. Most likely, Starbucks fell prey to the same sort of analysis mistakes that retailers commonly make when they attempt to optimize their product assortment.

Product analysis without customer analysis doesn’t work!

Here’s the trick. If you examine a product like the eggnog latte, you may very well see declining sales volume year-over-year and increasing costs. The increased costs probably come from the rising price of eggnog as well as the waste of eggnog spoilage. If you examine the product only by sales and cost measures, then the conclusion is inevitable – you should replace the eggnog latte with some higher-growth, lower-cost, greater-profit alternative.

But here’s what this narrow analysis of exclusively product costs and sales volume misses: That frequent purchasers of eggnog lattes were likely Starbuck’s best customers.  They purchased the eggnog latte as a relatively small share of their total purchases during the year, but probably a pretty high share of their purchases during the holiday season.  It was also an emotional purchase steeped in holiday tradition.

Guess what happens if you cancel that beloved eggnog latte? Your best customers “blow up.”

You see, the value of any specific product is actually NOT the value of the product by itself.   It is the value of the customers who purchase that product. If you take away a desired product, your customers may go elsewhere to get it, and they may not come back.

Suddenly you find yourself facing the customer acquisition dictum: for every one best customer lost, you need 10 to 15 average customers to replace those sales. And that math never works. As retailers roll along during this holiday season, it will be interesting to see which retailers have “optimize their product assortment” and which retailers have “optimized the value of their best customers.”

When you can’t find your favorite product at your favorite store, you will know the answer.

In my next post, I will talk about how you can figure out which products are so critical to your best customers and what to do about it.

Misconceptions of Multi-Channel Attribution

Posted by: liftpoint | On December 5, 2014

Cross Channel AttributionMarketing measurement is becoming even more important as marketers are under increased pressure to justify the impact of their spending.  But you can’t evaluate marketing touch points as standalone if you want to get a comprehensive picture of what is working or not.  Effective marketing often requires multiple communications to move a customer to purchase.

To identify what works or not, marketers are now delving into the area of “multi-channel attribution.”  The goal is to determine which combinations of marketing touch points and offers are the most successful at moving high-value customers towards the incremental purchase.

This recent Forbes article gives some background on the field of cross-channel attribution and the common misconceptions that marketers hold about this valuable type of analysis.

BTW — we are a consulting firm and we have experience in this area (despite what the author of the article claims!) :)

The heat is on for marketing organizations to demonstrate the value of their campaigns and show what worked or didn’t. This helps explain why brands plan to increase their spending on marketing analytics a stunning 73% over the next three years, according to the September 2014 edition of The CMO Survey published by Duke University’s Fuqua School of Business. For companies with $1 billion to $10 billion in revenue, the expected increase is even bigger at 86% – and for companies in the B2C services sector it’s nearly 100%.

The CMO Survey paints a clear picture of marketing organizations feeling more pressure to prove the value of what they do (65% say the pressure is increasing), but lacking the means to demonstrate impact in quantitative terms (about 65% say they can’t). For many brand marketers, attribution is the answer.

Done right, attribution can provide clear and accurate insights into how, when and where marketing influences customers across devices and channels. Marketers can then use those insights to spend smarter and define the optimal mix of customer interactions. In short, with cross-channel attribution, marketers can do more with less because they understand their customers better.

Read more from the source: Forbes


Weather or Not – Improve Customer Engagement with Weather Data

Posted by: liftpoint | On November 19, 2014

Big Data WeatherMany companies want to begin using Big Data, but very few know how to drive ROI from such efforts.  Red Roof Inn is using publicly available weather data to personalize communications to existing customers who may suddenly need a room due to weather-related flight cancellations. Weather data is one of the most easily accessible third-party Big Data sources, and often one that correlates well with purchase patterns. A good one to start with…

“Much of the conversation among marketers today focuses on Big Data. More specifically, how marketers can pare it down to make it useful. Leveraging open data–or free information such as government stats or weather information–is one way marketers are using Big Data to inform their campaigns in real time. In fact, marketers for hotel chain Red Roof Inn saw an opportunity to use this past winter’s weather data to turn travelers’ woes into valuable offers and increased sales.”

Read more from the source: Direct Marketing News


Can You Grow Short-term Revenue with a CLV Focus?

Posted by: liftpoint | On November 12, 2014

Customer Lifetime ValueChandni Vyas, in a post in Forbes, recently highlighted a study that Forbes has conducted with Sitecore, on the use of analytics to improve customer value.  This post focused on customer lifetime value (CLV) as a key metric used to “identify the value of a customer.”

“Using data and analytics has become a popular way to retain customers. This type of technology enables companies to collect data on an individual level. The customer lifetime value (CLV) metric is often used to identify the value of a customer.  It allows companies to balance the costs of acquisition and retention against future spending, and helps identify those customers that represent the highest future value.”

I have found that many companies struggle with how to focus on CLV in a way that helps grow the business in both the short and long-term.  CLV is by definition a long-term strategy (i.e. “lifetime”).  While these companies understand the strategic value of CLV, they need to concentrate on customer value in the current year to make their goals.

Their goal for marketing analysis is to improve the effectiveness and efficiency of their marketing budget; i.e. spend more money on customers with the greatest potential to increase value this year. While such an analysis can ignore long-term potential, our research has suggested that customers with improving 1-year value often tend to be customers who increase their long-term value as well.

Read more from the source: Forbes

Improving Customer Engagement with Big Data

Posted by: liftpoint | On November 12, 2014

Big Data Customer Engagement

In a CMSWire blog post titled How Big Data Can Make You a Better Marketer, Svetla Yankova uses the opportunity of Big Data to focus the conversation on how to improve customer acquisition by focusing on the right metrics, such as conversion of the right customers.

“Big data is everywhere these days. Among other things, it’s created some big expectations for marketing — especially when it comes to mining information. And while it may have the potential to change the game when it comes to data driven marketing, the reality is that it has yet to fully deliver due to a myriad of marketing methodologies clogging the funnel. What does this mean? Let’s back up for a minute. Before we can tap the results of big data, we need to examine the perspectives that are used to fill the funnel — growth and sales — and think about some of the fundamental shifts that are taking place. Then we’ll more clearly understand how big data fits in.”

While I agree with her discussion with regard to new customer acquisition, she fails to address one of the real opportunities of Big Data though.   Big data provides an important opportunity to increase engagement with current customers.  As much research has shown, retaining and growing current customers is easier and more profitable than large-scale acquisition efforts.

By leveraging Big Data (e.g. weather forecasts or construction) marketers can provide valuable services to their customers (particularly their Best Customers) in a way that will increase stickiness and long-term value.

Read more from the source:

5 Reasons to be a Minnesota Cup Mentor

Posted by: liftpoint | On October 28, 2014

If you spend any time with me, you will know these three things:

  • I get excited when our team at LiftPoint Consulting (formerly M Squared Group) figures out how to use a new approach to help our clients solve a tough problem.
  • I get pumped about new technology.
  • I enjoy helping young people get started in their careersMinnesota Cup

This summer, I was able to combine all three of the things I value by serving once again as a mentor for startup or early-stage companies entered in the prestigious Minnesota Cup competition, the largest new venture competition in the country.

After 5 years of serving in this role, and a little reflection, here’s why I enjoy serving as a mentor and what I’ve learned along the way.

The 5 benefits I’ve gained from being a Minnesota Cup mentor include:


Every time a new entrant selects me as a mentor, I’m guaranteed to learn something new. Last year I learned about the need for modest clothing among women of certain religions. This year took me to Southeast Asia (not literally) to learn about business card printing on palm leaves and water security/drip irrigation for rural farmers in India. These entrepreneurs show me a world outside of my normal life and open up my thinking for new possibilities. Often the most successful innovations in one industry come from another, and the techniques I see internationally have potential to help my clients here in the United States.


I have been running my company for over 13 years. We focus on the same industries, although with different strategies and technology.  But when these entrepreneurs tell me their stories, their businesses are both similar and REALY different!  They are so smart, so passionate about their mission and opportunity. It’s impossible not to have that excitement rub off. I leave my mentoring session excited for them and for with renewed excitement for my life and business.


When I spend time with entrepreneurs who are planning for explosive growth of their businesses, ideas come to mind for LiftPoint – ideas to serve our current and future clients in new and evolutionary ways. When I work with these startups, anything is possible.  Exponential growth is expected. How will they scale marketing, sales, production to accommodate dramatic growth in a short time period?  If growth is possible for them, why not also for me, our company, and our clients?  As a mentor I get a new set of glasses to look at my situation and re-imagine what might be.


As a mentor, it’s easy for me to see the shortcomings of these young business plans. Is the market big enough?  Is the expertise deep enough? Is the personality the right kind to attract investors?   My role as a mentor is to infuse additional confidence and point out spots that need more polish.

The examination of their business leads me to examine ours. What are our blind spots?  What is holding back our explosive growth?  How are my blind spots like those of these entrepreneurs?  If it weren’t for this mentor experience, I might not be asking myself these questions.


The Minnesota Cup rallies successful entrepreneurs from the Minnesota area. These professionals serve selflessly as judges, hosts for networking events, and, like me, as mentors.  During the six months of this competition, the entrepreneurial community is alive with excitement about the many new business ideas, and anticipation about which team will win the $300,000 prize.

What other business community in this country or the world rallies around young business dreamers the way Minnesota does? Sometimes I just have to take a deep breath of appreciation for where I am planted and the caring and selfless professionals around me. My Minnesota Cup mentor role fills me with such appreciation.

The next time you have an opportunity to “pay it forward” by mentoring a young person in their career, I urge you to do it.  The person you help will benefit from your wisdom, and in more ways than you can imagine, you will benefit too.

Bringing Marketing Analytics to a “Gut-Feel” Culture – 4 Approaches

Posted by: liftpoint | On October 28, 2014

Marketing analytics is today’s business oxygen. It’s ever-present, virtually invisible and the force underlying economic activities.  It’s so valuable that you’d think everyone would use it. But they don’t.  Only 32% of marketers use marketing analytics in any formal, routine way. *

For the other 68% of data-deprived marketers, a different thinking is prevalent.Data In Gut Feel Culture

“I trust my gut.”   “I know what works.”  “I’ve been at this a long time. I know what to do.”  “Data will only confirm what we already know.”  

Hmmmm. Really?

If that sounds like your organization, here are 4 ways to start breaking down that “gut feel” tendency.

1.  Use visualization to bring data to life

You may hear that data is too hard to understand or that rows of numbers are boring and confusing to everyone but quantitative marketers (and accountants).   Create charts and graphs that make those numbers easy to understand.  Trends and other patterns become readily apparent in a chart when they can be difficult to see in a table.   And popular Infographics are particularly effective at telling a data story.

2.  Preview results with key stakeholders

When you’re a data-driven marketer, you make decisions based on insight. But your approach can appear complicated and confusing to non-technical managers. Don’t surprise them in a meeting with data insights or results they have never seen.  To succeed, find time to share your data strategy and results in informal meetings with key stakeholders before big meetings. In these 1:1 interactions, it is easier for your stakeholders to ask questions and “kick the proverbial data tires,” without appearing confused in public. This approach makes data approachable (and you too!). And you might even create a data advocate in the process!

3.  Use test-and-control to show statistically significant lift

Marketing leaders are looking for measureable results.  MarketingSherpa’s 2014 E-COMMERCE BENCHMARK STUDY shows that marketers who test are more likely to have business success. Let me say that again: when you test, you improve your results.  So start testing!

Use control group to prove incremental behavior for your data-driven programs. Keep the timeline short. Analyze results for statistical significance. Base your next test on what you learn.

Test the position of different elements on your website. Test times of day, day of the week for sending emails.  Test colors, calls to action, headlines – every component of your marketing materials you think might impact results.  You’d be surprised how something like the position of pricing information on a web page can change results generated by that site.

4.  Create stories to bring the data to life

Many marketers are creative types who are experienced in storytelling.  Instead of showing trends, identify specific, personal examples and make them into stories supported by data. For example:

  • James is middle-aged father of 4 from St. Louis, MO who likes to do his own car and home repairs, but doesn’t have a lot of time or money. He is a one-stop, big-box shopper.
  • Jill is a 20-something professional who shops online for her office attire and furnishings for her Lincoln Park, IL studio apartment. She wants an online reservation system to schedule her car repairs.
  • Steve and his wife are empty nesters who drive around the Midwest to see their grandkids. Personalized, rapid car service is more important than cost to him.

Tell the story of “Mrs. Simpson in Biloxi, MS” who has been buying your product faithfully from her favorite drugstore for 15 years. Another consumer could be just-out-of-college, suburban-Chicago resident named Amanda who shops only online and is trying your product for the first time.  This approach puts a “face on the data” and helps managers visualize real customers in real-life situations.  Make data fun by using stories and watch the walls of resistance fall.

Now is the time for you to take a deep breath, try some new strategies and see those anti-data biases fade away. When you do, your whole organization will breathe easier, enjoying the success that a data-driven approach brings.

* marketingsherpa, E-commerce Benchmark Study, 2014