Every sport has its own lingo and scuba diving is no exception.  For example, you will hear one diver say to another, what’s the viz?  In other words, what is the visibility when you are under water. But what comes to my mind most often is “plan your dive, dive your plan”. My scuba instructor drilled that into my head.

Why do I think about this so often? It applies to almost every project I manage at work and probably the same is true for you. You need to create a plan for each project and then follow your plan!

When you start a project, you need to define it.  This includes creating objectives, outlining the scope of the project and incorporating feedback from the stakeholders.  Next you need to create the project plan which details how you will meet those objectives through discrete tasks starting from project kickoff until the final delivery.  Part of the plan will be a timeline with a cushion for the unexpected delays and problems that will inevitably come up.  In addition, your plan must include a list of deliverables and most importantly the KPIs that will tell you if you have been successful or not.  It is vital that you determine your success metrics at the start and that they are consistent with your objectives.

Once a plan has been developed, reviewed and agreed upon, you need to follow it.  So often interesting findings in the data try to lure me away from my objectives. It’s as if the data is populated by sirens calling to me as they did Odysseus, as he tried to return home to Ithaca. I don’t mean you should ignore interesting findings if they merit exploration but you have a choice.  You can revise your plan to reflect them or make exploration of these interesting findings part of a subsequent project or phase. Be sure to fulfill your original objectives before moving onto something else.

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An interviewer once told me he was thinking of creating a loyalty program and wanted to know what he should do.  Loyalty programs can be a valuable tool for driving revenue and fostering customer loyalty as I have written about here.  As I noted in another post, they can help you make smart marketing and merchandising decisions.

However, a loyalty program needs to be grounded by your business needs.  Ask yourself first:

  • What am I trying to achieve? Do I want to improve customer retention, drive revenue or encourage customers to purchase multiple products and services from my company?
  • How will I measure success? What metrics will I use to measure if I am achieving my goal and can I track those metrics on an ongoing basis?
  • Will my program be public or private? Some luxury brands enroll customers in loyalty programs based on their spending habits and reward their best customers without letting them know they are in a loyalty program.
  • Will my program have tiers? Explicit tiers can encourage customers to stretch and reach the next level.
  • How will I reward my customers? It is important that customers value what they receive in return for their loyalty.

There are significant financial considerations to any loyalty program so Finance will need to be involved in the development and design of the loyalty program.  But these questions will begin the dialogue about what the loyalty program should look like.  Once you have it in place, you should monitor the program’s health.

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Customers want to tell others about their experiences with your products and services.  Are you listening?

With text analytics you can quickly convert their unstructured feedback into data that can be analyzed for insights whether they be customer complaints about service, information about product defects or praise for a great customer experience.  This information can be used to quickly address problems and learn more about your customers.

There are a variety of Text Analytics tools available.  They will automatically categorize the text.  However, you will want to tailor the algorithms to capture key words for your industry or region.  If you have ever visited the Boston area, you may have been surprised by the number of times that the word “wicked” is used in conversation.  “Wicked” in this area is used to mean very as well as bad or evil. Similarly, your industry might have lingo that is special like “BOGO” which means buy one get one free.

Text Analytics tools will also enable you to extract key words or concepts.  For example, you might want to capture your brand or products and see what people are saying about them.  They will also determine if the sentiment is positive or negative.

Overlaying what your customers are saying with their attributes - their loyalty, lifetime value and experience with your brand - will enable you to understand and compare feedback across customer groups.  This will help you prioritize and tailor your response.  Combining customer data with customer feedback will provide a context for what your customers are saying.

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One of the reasons I enjoy being a member of a book club is that I get introduced to books I would not have chosen myself.  The current book club selection is Flight Behavior by Barbara Kingsolver which is the story of what happens to the residents of a small Appalachian town when Monarch butterflies unexpectedly migrate to their town.  Imagine my surprise when I read an excellent definition of the old adage, “correlation is not causation” in this novel.

The book before this one was A God in Ruins by Kate Atkinson.  The term chi square automated interaction detection appears in this novel about a World War II Halifax Bomber pilot, Ted Todd.  It is usually abbreviated to CHAID.  For more information on the technique, click here.

I wonder what will be in the next book!

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These days the debate over direct mail versus email seems to be over.  The conventional wisdom is that direct mail is too expensive and takes too long.  If a retailer has had bad sales over the weekend, they want to take action now and not wait a month or two to get a direct mail piece delivered to their customers’ mailboxes.

However, this approach could ignore some valuable customers.  What about your customers who are not emailable either because you don’t have their email address or they have opted out of email communications?  Also, sending an email doesn’t mean it will actually be seen by the consumer.  Google’s use of the promotion inbox makes it easier for consumers to ignore marketing communications.  In addition, plenty of people have secondary email addresses that they use just for these types of communications and which they check only rarely.

In addition, there is the question of whether email is always the best channel for the message.  A recent study found that physical ads were better than digital ads in some respects.  See here: http://www.dmnews.com/postal/direct-mail-has-a-greater-effect-on-purchase-than-digital-ads/article/423292/

In the end, it may be a multichannel strategy that works best for you.  Through a test and learn approach you can determine what generates the best return on your marketing investment.

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How important is it welcome customers to your brand?  If you are a brand manager with a welcome program for new customers, you may be asking yourself this very question.

Welcome programs run the gamut from a simple email that confirms someone has signed up for an e-newsletter or thanks a customer for making a purchase to  a coordinated series of communications across a range of channels (for example, telephone, direct mail and email).  The best welcome programs are integrated across channels and feature tailored messages based on the customer and the product or service purchased. These communications begin shortly after a customer has made a purchase, registered on a website, etc.  They can span as little time as a week to several months depending on the product.

Welcome programs are important for many reasons.  They enable you to:

  • Thank customers.  Let the customer know that you appreciate their business and it reinforces the good feeling they have about purchasing from you
  • Promote new products.  These communications can be used to make customers aware of additional products and services they may want based on what they have already purchased
  • Educate customers.  It is a way for you to communicate with your customers about product features that some may find confusing.  Proactively sharing with them how to access or use a feature could reduce future calls for technical support, saving your brand money and reducing customer frustration down the road
  • Understand your customers better.  It is an opportunity for you to gather information about the purchase process at a time when customers are most likely to talk to you

I have found that customers are most responsive to communications just after they have purchased a product and just before they are about to purchase again.  Take advantage of this opportunity to begin a dialogue with your customers.

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Big data has been a hot topic for several years and for good reason. There is value in analyzing unstructured, high volume and massive data sets. However, when I interview candidates that say they want to be data scientists, they focus on the technology and techniques.  They forget that the critical thinking and framework used for big data is also important and it is applicable to many types of analytic projects.

It comes down to some very fundamental questions:

  1. What problem am I trying to solve? Defining the problem up front will keep you grounded as interesting findings may lure you away from your goal.
  2. What data sources can I use? You want to consider multiple sources to triangulate your results and provided a richer picture of what is happening.
  3. Have I considered all the possible sources of bias? Bias of all sorts can skew results and must be considered and incorporated into your analysis plan.
  4. Do I need to use all the data available or will a sample be sufficient? There are times when it is not feasible or necessary to analyze all the data available. However, if you sample, you need to make sure that you are getting sufficient coverage and that your sampling is random.
  5. How can I validate my data? Validation must be part of your analytics plan, whether you validate one data set against another or at least compare your results to findings from other comparable projects.
  6. What analytic technique(s) are appropriate? Consider the pros and cons of various techniques and what would be most appropriate given the data and problem at hand.

While it is very tempting to dive straight into the data and analysis.  Spending time up front to answer these questions will help you be more efficient.

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I am often asked, what is big data? It happens at holiday parties and even once after a funeral. Certainly there have been large data sets before. So what is different now? Big data commonly refers to data that is so large that you cannot use the typical environments to store and manage it or the typical software to analyze it. In addition to volume, big data is often defined by velocity and variety. Velocity refers to the speed at which the data is available and big data typically includes frequent inputs.  Variety refers to the diversity of sources and formats and big data typically contains unstructured data which is not easily categorized or organized.

The volume of big data requires new thinking about where to put the data. Traditionally, companies kept their data in house, in a data warehouse on an internal server. Now some companies are turning to the cloud, both private and public clouds, to house data because of its size. In addition, the cloud offers flexibility should the needed storage capacity grow. Similarly, the volume and variety of the data may make it impractical to load the data into a database for to do so would require assigning data elements to tables and fields. Some big data may not be easily structured. For example, it could be text messages from online customer service chats. In this case, companies might turn to a parallel programming framework such as MapReduce to capture the data. This enables them to load all the data and then parse the text of the on-line service chats to identify the frequency of words used. For example, how many customers reported a problem with a particular part or described themselves as frustrated. However, you can’t use SAS or SPSS to analyze the data in a MapReduce environment. Further, data mining techniques may be more useful than classical statistics because of the nature of the problem to be solved. Thus, almost everything about big data requires rethinking data and analytic tools.

However, in the end, big data is like all data.   It must generate value. Big data is meaningless unless it enables companies to increase revenue and/or reduce costs by enabling them to identify insights that were previously unavailable. The power of big data is that analysts can explore larger data sets that were impossible to analyze before and delve into unstructured data that was typically ignored because of its non-conforming format.

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Thanksgiving Day is a time that I reflect on all the things I am thankful for and the increased emphasis on analytics is one of them.  With a struggling economy businesses want to know the value they are receiving from their marketing dollars.  Analysis can help them determine their ROI and optimize marketing efforts.  Increasingly, companies are looking at their wealth of data as a valuable asset that can drive revenue growth.  With data mining techniques, companies can identify hidden trends and insights that can lead to new customer segments or new product offerings.  Lastly, increases in technology have made it easier and easier to delve into data and display the results visually.  Thus, making it easier to uncover value in your data.

At a recent road race someone asked me what I did for work.  When I told her, with a smile, that I optimize marketing efforts through analysis, she remarked that it was nice to meet someone who enjoyed their job.  I enjoy what I do, in part, because I believe in the value of analysis.  It is very rewarding that others are recognizing that value as well.

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While everyone seems to be looking for data scientists these days, I have been interviewing Analyst and Sr Analyst candidates.  The focus lately has been on technical skills and rightly so.  Data scientists, for example, need to be able to transform raw data into analysis and actionable insights.  That may require experience with Java as well as data mining techniques, an unusual combination.

However, my interviews have reinforced the value of the intangible skills:  creativity, commitment, and curiosity.  The best candidates provide examples of how they have solved a problem creatively.  One candidate described a model he created to deal with missing data.  It is rare to have perfectly clean, comprehensive data.  Being able to overcome data issues is an important analytic skill.

I also look for candidates who are committed to providing the best possible analysis.  Clients pay me to solve difficult problems they can’t solve themselves.  They entrust me with their data and I take that trust very seriously.  Thus, I want everyone I work with to do the same.  While it can be difficult to assess commitment during the interview process, there are telling cues.  One candidate asked in advance about her interviewers, came prepared to her interviews with excellent questions that indicated she had researched the company and quickly sent thank you notes after every interview.  Her attention to detail and follow through made an excellent impression and spoke louder than words.

I always start an analysis with a hypothesis and a project plan.  However, sometimes during the course of an analysis, you find something interesting that changes your plan or analysis.  At other times, you may have a hypothesis that turns out to be false when pilot tested.  One candidate gave a presentation and at the end admitted that the marketing program she had developed did not generate incremental revenue as expected.  I liked that she presented on a program that did not perform as expected.  The only way to innovate is to remain curious and willing to test hypotheses.

While technical skills are important, don’t underestimate the intangibles.  I can teach someone SAS.  It is much harder to teach someone to be curious about data.

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© Lynne Harrold. All rights reserved.
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