Blog/Podcast

The LiftPoint Podcast: Amazon Prime Day

Posted by: liftpoint | On July 30, 2015

Highlights: Did Amazon Prime Day fail, or was it a online retailer success? Did Amazon compromise its relationship with its best customers? What lessons can be learned from the lesson of Prime Day?

Get your extra long shoehorns! #PrimeDayFail captures public sentiment over Amazon’s Prime Day on July 15th. Regardless of the less-than-stellar inventory selection and frustrated number of customers, Amazon did effectively influence consumer behavior and shake up the retail industry.

Not only did the massive online retailer capture an 18% improvement in sales over 2014’s Black Friday – in historically lackluster July, no less – and process an impressive 398 transactions per second, but they also created a ripple effect throughout the online retail segment. Having now marked July 15th as “Prime Day” for the foreseeable future, Amazon has created a paradigm in which other online retailers will feel the need to counter with their own promotions, or risk giving valuable retail ground to the competition.

True, Amazon may have shaken up the industry, but at what cost? Whether this was a publicity stunt to drive new membership of their Prime program, or a load test to gauge website capacity, could the mediocre selection of items offered have compromised Amazon’s relationship with its best customers, inviting them to shop the competition?

Long-term success will ultimately reveal the lessons learned on Prime Day. True, Amazon drove more subscriptions to its Prime program than any other day in their history, but how many of those members will stick around? How will Amazon drive customer retention? Looking at the short-term metrics may show that Prime Day was a resounding success, but how will such a broad-sweeping promotion without specific customer targets pan out in the future? With the cards on the table, Amazon will have to wait until next July for the competition to decide how it will react, how the landscape of midsummer marketing has changed, and the fallout of #PrimeDayFail.

Throwback to your favorite website circa ten years ago: www.waybackmachine.org

Check out Mark’s bio and Brad’s bio.

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The LiftPoint Podcast: Beginning with Big Data

Posted by: liftpoint | On July 30, 2015

Highlights: How is a marketer like a professional baseball team manager? Is data your most important asset? What insights can you find in this data, and what information is just “noise”? How do you get started with Big Data?

It’s time to play hardball with your analytics efforts. What do you and major league baseball teams have in common? The need for data. The St. Louis Cardinals are on to something, hacking the vast databases of the Houston Astros, Moneyball style, looking for statistical insights into their rival. Today’s baseball coaches rely on data to make decisions, much like marketers do: where once intuition was at the heart of decision-making, new data-dependent insights are fast changing the name and rules of the game.

When faced with something as nebulous and unwieldy as Big Data, the challenge for marketers becomes separating valuable insights from the “noise” in the data. A survey conducted by Teradata found that only 10% of marketers effectively use big data to drive their business, while 50% of marketers from the same survey said that data is their most under-utilized business asset.

How can you use Big Data to your advantage? By tapping into large, publicly available sources of data and cross-referencing those insights with customer behavior, it’s possible to make discoveries that help drive your business. Look at customer segments – who buys what, when they buy it, and where they’re buying it. Is the trend of retailers to push holiday promotional seasons earlier and earlier (think, Halloween in…July?) across all channels and customer segments effective, or is it eroding your value proposition?

Start your analytics projects with data you easily can access – you don’t need to overhaul your analytics process all at once. Use your data to identify different customer segments, and maximize customer satisfaction in those segments. Don’t wait for the perfect IT project to fall into your lap – get started leveraging data today.

Why would someone hack the Astros? ESPN article here: http://espn.go.com/mlb/story/_/id/13106874/why-houston-astros-database-worth-hacking

Read the transcript here

Check out Mark’s bio and Brad’s bio.

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The LiftPoint Podcast: How Marketing Has Changed

Posted by: liftpoint | On July 29, 2015

Highlights: In a marketing field where change is occurring constantly, how do you stay ahead? How do you adopt data-driven marketing as a way of life? What does the data say? How can you achieve quick wins to drive your business?

Introducing The LiftPoint, a new podcast featuring insights and actionable ideas about data-driven marketing. In the course of future podcasts, we’ll talk about the big-ticket issues facing the today’s marketer: Big Data, mobile marketing, analytics, organizational challenges, data science, and much more.

How marketing has changed!  The marketers of Mad Men days would be lost in today’s wealth of data.  Do modern marketers monitor their performance with the same rigor as warehouse managers track their shipments? So, with the incredible rate of technological change, how do you stay ahead, and keep up with your multi-channel customers? Start small.  Don’t bite off more than you can chew, or think you need to implement a grand, far-reaching strategy right away. Look at what you can do with the data you have access to today.

Today’s average marketer uses seventeen different systems to track key customer metrics, producing data ad infinitum.  The result – confusion about where start with all this data, and how to leverage it to grow their business. Your data-driven marketing strategy begins with the first step: tackling the data.

President Obama has assembled a tech team of Silicon Valley’s finest minds to help overhaul the technology infrastructure of the federal government – one project at time (think, the VA website debacle). You can do the same. Start small, building a portfolio of small wins based on actionable, achievable goals. Get the ball rolling by making data available, and working with it in small chunks rather than trying to change your corporate culture all at once.

Finally, use your data to build customer relationships. Try to show that you can be the “small town grocer”, the one who knows what customers need before they even tell you. In today’s on-demand economy, your customers dictate what they want, when they want it, and how they want it. Learning to adopt data-driven marketing as a way of life will help keep you at the forefront of your industry.

Check out Obama’s government tech team:  http://www.fastcompany.com/3046756/obama-and-his-geeks

Read the transcript here

Check out Mark’s bio and Brad’s bio.

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Don’t Let Your Marketing Analytics Project Fail – Part 2: Technology & Data

Posted by: liftpoint | On July 1, 2015

Marketing AnalyticsNo project plan includes a project management line item for “failure”. Yet with marketing analytics projects, there are some common areas that frequently lead to failure. Knowing where those trouble spots are ahead of time, and what to do about them, puts you in the driver’s seat for project success.

This month, we discuss technology and data risks to marketing analytics project success. Last month’s focus was on the human factors that can derail a project. Next month we look at organizational roadblocks. Always with how-to practical advice for overcoming these weak links in your Marketing Analytic project implementation.

Analytics by definition are rooted in technology and data. Problems in either area can result in missed deadlines and confusing, even useless, results. Following are some of those issues and the all-important how to solve them.

 1.UNFAMILIAR, DISORGANIZED DATA

The first time that your analysts begin to work with new data sources, and attempt to stitch them together can be a frustrating experience. Not only will your team be unsure of the definitions of different data elements, but often the data will be difficult to combine. Substantial investigation will be required to determine which fields include what data. In addition, data linking approaches must be identified and tested.

When marketers rush the analysts at this point, mistakes happen. Substantial analytic work in the wrong direction is another mistake-filled area.

Alternative Approach:

At the start of your project, you can shine a light on how the group plans to deal with unfamiliar data. Just by talking about it can eliminate a whole host of issues. A data dictionary is another tool to help assure a common understanding of the sometimes-misunderstood data language. Involving marketers in the data validation phase of the project also helps assure success. The numbers simply have to make sense. Once you are sure that the data adds up and stands for what you think it stands for (e.g. tot_rev_gross = total sales dollars before discounts), then you can move the project ahead.

The next project-failure-insurance step is to make sure that the analytics team understands how the project results will be used. Particularly, the analytics team needs to be aware of the level of accuracy that the marketers will need in order for their intended action to be successful. For example, the first email program that you send to a new group of customers may not require that every customer has a valid email address; rather, if 50 or 60% of customers have valid email addresses, the resulting information may be very useful to a marketing program. Communication is the key to making sure that the analytics team isn’t operating at a higher level of precision than marketers require to be successful with their marketing efforts.

2.  CONCERNS ABOUT DATA QUALITY

Purists on your team are sure to complain about data quality – even refuse to work on data that isn’t up to certain standards. “How can we be sure the data is clean?” they often ask. Of course, any manually entered data has its share of issues and deserves some level of correction before any analysis takes place. But taking the time and effort to find and fix every error is not in the best interest of your Marketing Analytics project.

Other data quality issues arise when distinct and different data sets are merged, which by definition happens in nearly every Marketing Analytics project.

Alternative Approach:

While the goal of a Marketing Analytics project is not to correct every instance of incorrect data, it can be very valuable to identify which data is incorrect and which data we believe to be accurate. If the analytics team can identify which data appears to be incorrect, that data can be exported from the analysis quickly, permitting the analytics team to move ahead and the marketing group to get the information they need in a timely manner.

3. PURSUIT OF THE PERFECT STATISICAL MODEL

Statisticians can get very excited about refining models in order to get a more precise answer. The risk is that the additional level of precision will not be valuable and the time necessary to refine the model will unnecessarily delay the project.

Alternative Approach:

Make sure that the marketing team is involved in reviewing the initial statistical model and understands the strengths and weaknesses of the existing work compared to additional refinement. If a model predicts improved results of 40% compared to a random customer group, those results may be more than sufficient to deliver a winning marketing program. The difference between a model that predicts a lift of 40% compared to a model which depicts a lift of 48% may be irrelevant to the definition of your marketing project’s success. Only when marketers dig in and understand the results of the initial model can the marketing team make sure that the statistics do not get out of control.

4. LACK OF VISUALIZATION TOOLS

When all of the data manipulation is complete and it’s time to communicate the results and learning, the format used is often a never-ending set of Excel workbooks. When you have dug deeply into the data, it can be very exciting to you to show all the details and interconnections to your audience. However, for your audience, such a level of detail may be mind-numbing. Some marketers are highly quantitative and will enjoy rolling up their sleeves and getting into the data at the detailed level, while other marketers (as well as salespeople) need to be shown the bigger picture in a simple way first, before they ask for a limited set of details.

Overwhelm them at the start with excessive detail across pages and pages of numbers, and you risk “losing them at hello.”

Alternative Approach:

The key to success will be to understand what the critical pieces of information are for your audience, and then displaying that information in the most compelling way possible. When done well, visualization and infographics can become a living legacy for the work you have done – shared across the organization and from office to office.

In the past three years, data visualization tools have become more commonplace. There are more, simplified and open source tools than ever before – tools that turn data into graphically beautiful charts, graphs and pictures.

The key will be to first understand the information that your audience needs, then develop some rough ideas on how to display that information in a compelling manner. Only then can you review data visualization tools available in the marketplace today and choose the appropriate tool to fashion a story that no one will forget.

On your next Marketing Analytic project, pay particular attention to the all-important technology and data components. Now, when you stumble upon one of these common roadblocks, you’ll have some strategies for steering a path to success.

Try some of these ideas and let me know how they worked for you.

Don’t Let Your Marketing Analytics Project Fail – Part 1:The Human Factor

Posted by: liftpoint | On June 8, 2015

iStock_000023659928_SmallYou thought you were doing everything right.  You found and gathered data, invested in tools, got budget, and maybe even hired a data scientist or at least an internal analyst.  But you find yourself running into roadblocks that you didn’t anticipate when you championed bringing data analytics into your marketing department.

Trust me, you are not alone. Gartner Research’s recent report “Predicts 2015 – Big Data Challenges Move From Technology to the Organization” predicted that through 2017, 60% of big data projects will fail to go beyond piloting and experimentation, and will be abandoned.

In over twenty years of marketing analytics consulting, I’ve seen my share of these data-driven marketing projects (Big Data and otherwise) go awry.  Sometimes changing corporate or budget priorities derail a project mid-stream. Other times they just painfully limp along.  Worst of all are the projects that fail to get off the ground at all.

In this first article of a three part series, I will focus on the most common of those project-failing reasons – the human factor.

It’s those pesky people!  What you imagine to be the hard part of an analytics project – the analytics – turns out to be the easy part.  Aligning the marketing team with the analytics team is essential but often difficult.  Without it your project will move 2 steps forward and 3 steps back over and over again.  Eventually, both groups retreat to easily defined work and the opportunities to use analytics as a significant business driver are lost.

Not surprisingly, interpersonal problems tend to arise when highly accomplished professionals in very divergent specialties work closely together, each with their own language, priorities and expectations.

Here are three reasons that the human element can derail marketing analytics projects, and what you can do about it.

1. VAGUE BUSINESS QUESTIONS

A business question is the driving force in any marketing analytics project.  When the business question is too vague or not present at all, the project is doomed from the start.   A poor definition by marketing of their key business question can easily lead to misunderstandings in the analytics team.  Those misunderstandings can lead to the analytics team needlessly burning a lot of calories in the wrong direction and not having useable, actionable results at the end.

Alternate Approach: 

Assure success by gaining stakeholder alignment on a measureable, deadline-driven, clear business question.

This process begins by getting ALL possible business questions out on the table, from leaders in any department participating in the project.  Then, work together to agree on THE big critical question, possibly with a few tangential “what if” business questions.

Asking and documenting answers for questions like the following will help keep all project participants focused in the same direction:

      • What does success look like?
      • How does this project align with priorities of the project sponsor?
      • How will this data drive business once the project is complete?
      • What business deadlines drive this project?

An example of a vague key business question is “What are the characteristics of our best customers?”.   A much more actionable, clear question would be “Which of our best customers are most likely to leave and which offer will likely get them to purchase again during the holiday season?”.

 2. ELUSIVE TEAMWORK

This is probably the most common and “worse case” area where a marketing analytics project falls apart.  Some analysts march to their own drummer.  They spend excess time trying to arrive at an answer that is 100% accurate, get caught up in interesting, but insignificant-to-the-business insights, are uncomfortable with approximate results that make more business sense, and don’t respect “imprecise marketers willing to settle for inferior answers.”  Another issue is that marketing and analysts often speak different languages.  This leads to misunderstandings and ‘analytically perfect’ results that no one in marketing can understand or implement.

Without teamwork, the analytics team tends to “work in a vacuum,” moving from an agreed-on assignment to a weeks- or-months-later answer that doesn’t match the business team’s expectations.

Marketers cause their share of teamwork problems, too.  They can have a condescending air toward analysts, change their minds about projects in midstream, expand scope, be unavailable or unwelcoming for questions or generally make the analyst’s job more difficult than it needs to be.

Alternate Approach:

If possible, marketing and analytics should office next to each other.  This physical closeness helps to facilitate a closer working arrangement. The approach that works is not a number of big review sessions where lots of work can be showcased, but a constantly iterative process, where project approach can be tuned based on findings as they emerge.  Daily or bi-weekly informal review sessions can reduce the number of missteps and improve the speed to market for results.

3. LACK OF COMMON GOALS/METRICS

A compounding factor that drives inefficiency between marketing and analytics is a difference in metrics, particularly goals that drive compensation.  Marketing goals should be focused on key business metrics, such as traffic, acquisition, cross-sell and retention.  Analytics metrics can be more operational and educational – complete X number of analyses, produce X number of reports accurately and on-time and learn a new modeling technique.

The gap between the two types of goals means that marketing is pushing hard for analysis that can drive action during the year, and analytics is more concerned with production and accuracy.

Now, accuracy is always important – don’t get me wrong.  But no analysis is 100% accurate and decisions must be made between additional analysis and time to market.

Alternate Approach: 

By mirroring metrics across marketing and analytics, you will clearly change behavior.  Analytics will understand the impact of getting the right answer in a timely fashion in order to hit metrics that will impact everyone’s paychecks.  Both teams can meet and have an orderly discussion about analytic techniques, their benefits and time/resource costs, and make project decisions together.

Remember, change compensation and you change behavior.

While every business project has rough spots, marketing analytics projects experience more than their share of difficulties. In upcoming articles I will discuss how technical and organizational issues also can obstruct progress and success on these exciting and challenging projects.

In the meantime, try some of the solutions mentioned here to give your marketing analytics project the best chance for success.  And let me know how it works for you.

 

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.

1.  COMPUTER SCIENCE

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.

2. APPLIED MATHEMATICS

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.

3. STATISTICS

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.

4. BUSINESS/MARKETING

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 Starbucks.com

From USA Today

From Time.com

From Huffington Post

From Today.com

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