Know if this assortment works at other locations with analytics.
Retailing holds great lessons for marketers in all industries because as long as there have been customers, retailers have focused on the customer experience. And one of the biggest elements of the customer experience is assortment - literally addressing the customer's need for variety or choice. Think of a time when you weren't exactly sure of what you were looking for - didn't you go first to places you knew had a wide variety? That's assortment.
How do retailers manage assortment? When you layer on the challenge of multiple locations, you can see how the issue quickly expands. In order to deliver a consistent customer experience, many retailers maintain a standardized layout or “floor set” among their locations - either keeping the same assortment in all locations, or varying by location. Neither approach is always better than the other, but in either case the question is how to best vary the product offerings to deliver the best customer experience.
That concept is also known as localization, and it can be quite a challenge without analytics. Early in my career, I would pull category level sales by location and put it into a spreadsheet. Then I'd sort it and make a note of the top locations by category and use that for decision making. I now know how inadequate that was. To complete the picture for you, when an order for a specific category arrived in to the distribution center, I would refer to one of the hundred sticky notes attached to my computer monitor and manually make sure that the locations received the product.
Not only was my little process painstaking, it fell short in accuracy, it was clearly inefficient, and it definitely was not scalable. With that old approach, a top volume location would likely end up on the top of the list for every category, but a lower volume store may not even get a sticky note. Sticking strictly to my sticky notes would mean ignoring the need to create a great customer experience in every location, regardless of sales volume. Quite a problem, right? But it's not insurmountable. Read More »
In the movie Minority Report, while the leading actor walks through the mall and experiences personalized greetings all around him, there is a clear flash of how the future of marketing may look: a customer journey marked by relevant and personalized experiences.
Getting back to the reality of today, the typical customer journey represents various customer interactions with your brand over time across all the digital and offline channels. When they are done right, each of these touch points builds on the others to play an important role in bringing your customer closer to choosing your brand over others.
The customer journey: what it takes It’s the “when they are done right” part of that scenario that requires a great deal of effort, particularly with regard to:
Personalization: Increasing response rates with uniquely tailored content, delivered instantly.
Automation: leveraging content across channels and doing it consistently.
Optimization: using real-time analytics to drive the best possible outcomes for each customer and for your organization every time.
Innovation: Finding ways to rise above the noise by promising – and delivering - compelling user experiences.
"Our corporate data is growing at a rate of 27% each year and we expect that to increase. It’s just getting too expensive to extend and maintain our data warehouse.”
“Don’t talk to us about our ‘big’ data. We’re having enough trouble getting our ‘small’ data processed and analyzed in a timely manner. First things first.”
“We have to keep our data for 7 years for compliance reasons, but we’d love to store and analyze decades of data - without breaking the machine and the bank.”
Do any of these scenarios ring a bell? If so, Hadoop may be able to help. In this 5-part blog series, Big Data Cheat Sheet on Hadoop, we’re taking a look at five big data questions from the perspective of a marketer. This post answers the second question in the series to help marketers understand how these big data technologies are impacting (or can impact) the customer experience, and what you can do to take advantage of this data playground.
Question 2: Why do we need Hadoop if we’re not doing big data?
Contrary to popular belief, Hadoop is not just for big data. (For purposes of this discussion, big data simply refers to data that doesn't fit comfortably – or at all – into your existing relational systems.) Granted, Hadoop was originally developed to address the big data needs of web/media companies, but today, it's being used around the world to address a wider set of data needs, big and small, by practically every industry.
Why are they thinking that? And how is their customer experience different with mobile? As the world is going mobile, those were the questions on our mind as we kicked off an important research project with Northwestern University’s Kellogg School of Management (the Research). We were motivated to zero in on those questions because of all the technological advances that have transformed marketing of late, mobile is perhaps the biggest game-changer.
The advent of smartphones, tablets and wearable devices are key engagement portals that are driving a fundamental paradigm shift in how customers engage with organizations. As a result, how marketers engage with customers has to undergo an equally radical change.
Mobile is reshaping the customer experience The Research indicates that the vast majority of respondents’ customer experience via mobile with businesses are still primarily passive, but are also highly influential on other interactions as evidenced by these specific findings:
Three out of four respondents engage in showrooming, and a similar proportion uses their mobile device to check bank account and credit card balances,
Roughly six in ten respondents use mobile to add an item to an online shopping cart for later purchase via laptop, and
Over half of the consumers responded that they are opting in via mobile to receive information as well as promotional offers from businesses.
As a customer intelligence adviser, my work exposes me to a wide range of organizations with various marketing challenges and available resources.
Steps to mastering omni-channel marketing
Over time, some common themes have emerged, one of which is omni-channel marketing as a business imperative due to the explosion of channels and the evolution of customer behaviour.
The way organizations have approached omni-channel marketing seems to fall into a five-step pattern, so that inspired me to write this five-part blog series titled, Five steps to omni-channel marketing.
In the first post in this series (Five steps to omni-channel marketing-step one) I explained how you can move as an organization from mass marketingto segmented marketing. In this post I will explain how you can take the second step to move from segmented marketing to one-to-one marketing. Let's start by examining just what one-to-one marketing is.
Step 2: From segmented marketing to one-to-one marketing
Recently, I was given the opportunity to present a session titled, An Executive’s Cheat Sheet on Hadoop, the Enterprise Data Warehouse and the Data Lake at the SAS Global Forum Executive Conference. During this standing-room only session, I addressed these five questions:
What can Hadoop do that my data warehouse can’t?
We’re not doing “big” data, so why do we need Hadoop?
Is Hadoop enterprise-ready?
Isn’t a data lake just the data warehouse revisited?
What are some of the pros and cons of a data lake?
I've been inspired to re-think my answers to those 5 questions in terms of the customer experience and present them for marketers as a 5-part series in this blog. My goal is to help marketers understand how these big data technologies are impacting (or can impact) the customer experience, and what you can do to take advantage of this data playground. Let’s get started!
Question 1: What can Hadoop do that my data warehouse can’t?
Here’s the short answer: (1) Store any and all kinds of data more cheaply and (2) process all this data more quickly and cheaply.
The longer answer is:
[Please excuse me as I step up on one of my big data soapboxes to address this question.]
I’m here to tell you that big data is not new. Yet, with all the hype these last few years around these two little words, you’d think we’ve discovered the Holy Grail. Let me share with you the dirty little secret about big data: it’s just data—the same data we’ve had for decades.
Imagine that your business operates with over 50,000 employees in over 60 countries, you are the leading manufacturer in the world for your flagship product, and your customers can be found quite literally world-wide. Then imagine having a goal to become more agile and customer-centered across the board. That clearly describes a big data a scenario, which as usual, holds both challenges and opportunities.
This scenario describes Lenovo, and the leading technology company is running big data analyticson the public cloud infrastructure of Amazon Web Servicesto capture the voice of the customer. They combine insights from their own data with unstructured data, such as social media, product reviews, customer forums, call center logs and online chat sessions. This approach enables Lenovo to use visual analyticsto pick up on issues earlier, giving them a chance to address them quicker. The result is that what once took them 60-90 days to identify and respond to quality issues now happens in a matter of weeks.
Not all organizations are quite as big as Lenovo, but big data is relative and with analytics, the size of your company or the complexity of your business do not have to stand in the way of your hearing the voice of the customer. Tune in to this video below to hear directly from Lenovo executives about how they capture the voice of the customer.
Let us know what you think. And as always, thank you for following!
Our customers are networked - they connect with people, places and things that matter to them. That concept and understanding what to do about it holds the key to how organizations can improve customer experiences.
Great customer experiences: hard to depict, but unmistakable when you see it.
It's a matter of networking your organization, so making it more agile, more enabled and more responsive. Doing that is a worthy goal, but far easier to say than to do. Microsoft introduced the catch phrase of “working like a network” through a video for its enterprise social offering. But my view is that “working like a network” goes beyond just the social channel -- it permeates every department within the entire organization.
Given that thought, just how does an organization transform the way it works? Let’s look at the three key components of “working like a network” that Microsoft mentions and consider how we can put them into play to improve customer experiences.
Integrate Data Management and Analysis
In order to anticipate what will come next for your organization -- whether it’s from an employee, customer or partner -- you have to answer several questions. What happened in the past and why? Can we forecast or predict what will happen in the future based on the latest information we have? And finally, can we prescribe ways to address issues and tackle challenges that may arise in the future based on this information?
In order to answer those questions, organizations need three key ingredients: Read More »
“Is yoga a sport?” Years ago, an executive at Adidas found himself pondering this very question. At the time, the sports design paradigm centered on competitive edge and athleticism. But, not every customer is a professional athlete. Where did customers engaged in recreational activities fit?
Also for yoga (and so much more).
The “yikes” moment for Adidas was realizing that the industry model was incongruent with reality. After looking at their existing data critically, they still fell short of answering his question. The historical data supported the current paradigm and didn’t reveal any new insights on yoga as a sport.
Adidas turned to Christian Madsbjerg and Mikkel Rasmussen for help. Together they embarked on a new investigative journey. They collected new, largely qualitative data on what drives customers to buy and use their products. They wanted to know what motivated people to participate in sports like yoga, biking, or jogging.
As the world around us changes, so do we. Smart companies, like Adidas, learn how to tap into and capitalize on these changes. Here are some important lessons we can all learn from Adidas:
Challenge the way you view your customer
Today, roughly 20 million Americans practice yoga. If Adidas had refused to expand their view of the customer, they would have missed out what is now a major market. Adidas’ willingness to explore and rethink their customer view led to some major “a-ha” moments. Innovative companies do this regularly. Read More »
Optimisation techniques are used in a variety of business contexts to find the best combinations that deliver the desired results, often measured in terms of value added from maximising revenues, minimising expenditures, or both. In marketing, a frequent problem that's well suited to optimisation is when one has:
Optimisation creates value from confident data-driven decisions.
Many customers (often millions of them) ,
Multiple potential offers,
Rules that determine the number and frequency of offers,
Resource constraints (such as budget), and
The need to maximise something, such as sales or profit.
Optimisation quickly gets complicated because to find the best solution, one needs to consider all the combinations that exist, and with millions of customers and hundreds of communications, optimisation is not an easy problem. SAS solves this with SAS Marketing Optimization (SAS MO), but the principles surrounding the challenge that I am outlining here are relevant to any of the so called ‘large scale’ optimisation challenges.
Often people think that they need many models to justify using optimisation techniques, or to get the best out of the optimisation. So whilst the latter is partially true, I really don’t think that the former is, especially when considering what one is trying to achieve. I would even go as far as to say that you can get significant performance improvements with optimisation, even if you have only a few, or even no models, (none, zilch, zero!). Let me try and justify this:
The uplift in optimisation solutions, when compared to how a BAU (Business as Usual) approach is performing depends on three core factors: Read More »
Welcome to Customer Analytics, a blog for anyone who is looking for ways to improve the business of marketing and communicating with customers.
We strive to prompt new thinking in the way you tackle customer-related business issues. And we hope to inspire the use of analytics for everything from multi-level marketing to social media campaigns. Follow us here and on Twitter at @SAS_CI, or check out the Twitter hashtag #sasci.
I’m John Balla, Editor of the Customer Analytics blog and Principal Marketing Specialist in Customer Intelligence. Read more about me here.