Have you heard the story of the blind men and the elephant? In this famous Indian legend, a group of blind men touch an elephant. However, each man feels just one part and it is a different part of the elephant for each man. They compare notes on what they felt and are in complete disagreement. In many ways, this is how the customer is seen by some companies. The digital marketing team has one view of the customer, the product marketing managers have another view and creative might have a third view. Here are five reasons why you should hire a Chief Customer Intelligence Officer who will integrate and disseminate insights for a holistic customer-centric approach:
1. Grow revenue. An integrated understanding of your customers and their journey with your company will enable you to up-sell and cross-sell effectively to them. Only with a comprehensive view of the customer will you know whether he wants more of the same or if he needs something different. Rather than the product managers focusing on promoting their products and meeting their sales goals, customer preferences and needs would take precedence.
2. Reduce acquisition costs. Consolidating insights across channels and products will enable you to segment your customers by purchase history, demographics, lifestyle, lifetime value, etc. Thus, you can provide the right message to each segment and find new customers who look like these segments. With better targeting and identification of your best customers, you can find new customers who are similiar.
3. Enhance customer retention. Customers expect a coherent and consistent customer experience across channels. If you integrate insights and provide an experience tailored to their needs, behavior, and attitudes, they are more likely to be retained and become advocates of your brand.
4. Improve campaign performance. Customer insights from the direct marketing channel can inform strategies used in the digital marketing channels and vice versa. For example, you could re-target visitors to your website or social media advocates via direct marketing.
5. Increase customer satisfaction. Customers will reward your focus on their needs and preferences with increased satisfaction and willingness to recommend your brand to others.
Remember the days when we asked ourselves, “What is the value of a like? A tweet?” The debate is over. A social media strategy is a requirement for any business. However, Social Media Specialists can apply best practices used regularly in other marketing channels to enhance the effectiveness and ROI of their campaigns.
Targeting. It is important that you identify the best targets for a promotion using a data driven approach. You can leverage your CRM system and use customer insights to target your customers online who are are most likely to respond to your social media campaign. Alternatively, you can use customer data to identify the conquests on-line who look like your best customers.
Post campaign measurement. Just because it is social media, doesn’t mean that you shouldn’t consider control groups and incremental lift. Here’s your chance to demonstrate the value of social media as a channel using the same rigorous methods as email and direct mail.
Combining data and insights across channels. Why not append your customer’s social media interactions to their off-line attributes and all the other customer data you have? One client found that some of their best brand advocates on-line shopped mostly in stores. Without linking off-line and on-line behavior, you don’t have a complete view of your customer. For example, you might be tempted to remove these best customers from your online communications because they don’t shop online; however, in this case, email was driving them to the store!
Are you taking advantage of the analysis tools and approaches that work in traditional direct marketing to enhance your social campaigns?
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!
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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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.
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.
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.
The new year has begun. Now is the time to measure the success of your holiday campaigns. How did your campaigns perform? This is an opportunity to look at their effectiveness in terms of building awareness, generating revenue, increasing retention and aiding customer acquisition? How do your metrics compare to industry benchmarks as well as internal benchmarks? How much revenue did they generate and were they profitable? In addition, what worked and what didn’t? Now is the time to evaluate any tests that were done – date/time, subject line, creative, etc. Finally, compare the results of this past holiday campaign to the one before and analyze the differences. The insights from the holidays can inform your strategy for 2012.
Do you spend a lot of time on YouTube? If so, you may have already seen this but I was surprised to see the MC Hammer video on YouTube. Am I the only one surprised to hear the words “behavioral targeting” being spoken by MC Hammer? Who knew that MC Hammer and I would have something in common. We both believe that analytics enables you to allocate your marketing dollars effectively.
If you haven’t seen it, watch MC Hammer on Analytics from YouTube.