Innovation promises to enrich the retail customer experience. However retailers must meet rising challenges to deliver these experiences securely. At EKN Research’s most recent retail executive dinner, senior Marketing, IT, and Loss Prevention leaders discussed this delicate balancing act.
Let’s begin by recognizing customers on the endpoints of the security/experience continuum:
- “I’ll Tell You Everything” – This customer craves the experience and emotional connection that comes with shopping at her favorite retailer. She rarely, if ever, thinks about security – expecting that someone else has it covered. She is trusting…especially with her favorite brands. Her expectations are high and
,she demands relevance and service in exchange for her continued trust.
- “I’m Not Giving You Anything” – She pays in cash and won’t join your loyalty program. She’s disabled location services on her phone and doesn’t post to social media. She craves her privacy. Maybe she’s been burned before or perhaps she’s read about one breach to many. She won’t compromise privacy to get a better customer experience.
In reality, most customers fall between these two extremes – so retailers must consider both.
Now, let’s try to wrap our heads around the pace of innovation. New brands, concepts, products, services and experiences continuously evolve across web, mobile and store channels. The buzz from business and technical ideas is constant –personalization; customization; wearables; IOT; cloud; and more potential 3rd party partners and vendors than you can count! The rise of analytics and data-driven decision-making elevates the need to harden data security and integrity more than ever.
External security concerns (many beyond a retailer’s direct control) include POS or credit card breaches, denial of service attacks on a retailer’s systems (or those of partners), competitive espionage, social or political activity, and more.
Insider threats can be just as dangerous – shrink, leaks of sensitive non-public financial data, or the usage of unsecured technologies outside of corporate firewalls. And unfortunately physical security of both stores and home office locations for active shooter scenarios are now commonplace.
The risk environment can seem daunting!
But retailers cannot go into a shell and wait for these storms to pass. The pace of change and the need to stay relevant (and profitable) requires balance. How does one maintain the appropriate level of security while at the same time implementing new ideas and experience to engage customers?
It starts with ensuring a culture of partnership. Loss Prevention and Security cannot be effective operating within a “cafeteria culture” – one where they are lucky enough to overhear about potentially risky initiatives while in line at the salad bar. These groups need to establish their position as a trusted advisor, where they are brought into the risk evaluation process up front to effectively articulate the risk probability and range of outcomes.
Here are a few more practical suggestions for maintaining this delicate balancing act:
- Don’t collect or retain more data than you need. Extraneous information doesn’t help and if breached, only increases downside risk.
- Establish more than a purely subjective evaluation. Perhaps you’ve had a past event with a known financial impact that you can reference – or create a quantitative model that can predict ranges of potential outcomes.
- Understand your customer and your culture and use them to define your risk appetite. Value the expertise from all perspectives, but also ensure clear decision-making authority and accountability.
Finally, ensure that the memorable customer experiences that you create are positive – you’ve either delivered on the emotional connection and needs of your customers or you’ve effectively avoided serious risks.
And of course, keep your balance to avoid ending up on the front page for the wrong reasons!
Recently, EKN Research hosted a dinner where omni-channel, analytics, e-commerce and store operations executives from leading retailers in Columbus, Ohio gathered for an exploratory discussion on using analytics to drive a richer customer experience in store. Our goal was to understand the keys to deliver a differentiated experience, deepen the customer relationship and achieve their objectives.
We began by talking about our favorite retailers who deliver a personalized and effective customer experience in store. As we listed them, two common themes came to the surface:
- Genuine intimacy and personalization delivered by associates that have deep product knowledge and shared passion with the customers were important. Local wine shops and farmer’s markets that were members of the community and cared deeply for their product differentiated themselves. Associates that shared a passion (e.g. running) drove experiences and loyalty that were memorable beyond the transaction.
- Convenience, efficiency and effectiveness were hallmarks of the most common retailer cited – to nobody’s surprise despite their lack of stores – Amazon.
As the discussion continued, we quickly realized that there are many potential objectives. Are we trying to increase traffic? Improve customer service? Increase conversion? Engage with customers? Communicate a new product launch? Are we trying to empower our customers, associates or partners – or all of them?
Analytics (in store or elsewhere) are useless without a specific business objective, a decision that needs to be made or an action that must be taken. We need to first define our goal and understand the desired outcome.
For example, should we seek to deliver genuine intimacy and personalization (which takes time) or deliver a convenient and efficient transaction to the customer? Can we do both?
We cannot determine how to deliver this richer customer experience without understanding context. Defined as “the circumstances that form the setting for an event…in terms of which it can be fully understood and assessed”, understanding the context of a specific customer for a specific shopping journey will enable us to deliver the customer experience that they expect at that point in time, engendering deeper loyalty and satisfaction.
This is no easy task. It is made more difficult by the fact that context can be different for the same customer at the same retailer, even shopping for the same product! Is she shopping for herself or for a gift? Is she just gathering information or is she ready to buy? Is she a repeat or first-time customer? What time, day, date, or season is it?
Stores remain the hub of the overall retail experience yet lack the metrics and analytics of the digital experience. Investing in processes and technology that help us understand the many dynamic contexts within which our customers live, both in-store and online, will help retailers be more responsive and effective.
Sometimes we’ll deliver a highly personalized, highly consultative experience. Other times we’ll provide curb-side BOPIS delivery. Sometimes we’ll call the customer by their name and other times we’ll respect their anonymity. Each time may leverage a different tactic or channel but will deliver a satisfied customer.
It all depends on context.
Earlier today, I read a good article entitled “The Art of Telling a Story In Analytics“. Not only did the author correctly identify communication as one of the most critical elements of analytics, but he also recognized the significant challenges that most face in that regard.
Consider the following simple construct of good story telling elements as it is applied to analytics:
1) Theme – The theme is what the store is trying to tell us. In analytics, this must be the actionable decision that will be made as a result of the analysis.
2) Plot – The plot represents the main conflict or struggle of the main character. In analytics, this should represent your hypothesis and supporting tests that you plan to execute.
3) Structure – The structure of a story defines the perspective (e.g. first person, third person) and tense (present, past). In analytics, structure should define the timeframe, duration, and context to be analyzed.
4) Characters – The characters are the heart of a good story, those around whom the events revolve. In analytics, the characters are the data elements. Like in a good story, they must be well defined and well understood. And usually many characters interact to produce a compelling narrative.
5) Setting – The setting represents the place and time in which the story takes place. In analytics, is is the same. But also consider elements of setting to include business performance, competitive positioning, or other elements of the business environment (e.g. merger/takeover, startup).
6) Style And Tone – These represent the language, words and actions that are appropriate for your story. In analytics, this represents the same, but with consideration to your brand positioning and desired customer experience.
By applying these elements to analytics storytelling, you’ll be able to gain the attention of your audience and explain the data and resulting recommended actions in an easy to understand manner.
I’ll leave you with a two important points below…
There is a very important difference between an analytics story and other stories. In a normal story, interesting is good. Interesting captures the reader’s attention, keeps them gripped to the pages and makes for an very enjoyable reading experience. In analytics, interesting does not help. As cited in the referenced article, knowing that a specific customer segment “tends to have many products, have not received many promotions , tend to buy financial products , are credit worthy and tend to live in Quebec” is merely interesting. It does not help drive a decision or business action. In analytics the Theme (decision) and Plot (hypothesis and tests) are critical to an effective outcome.
In a prior post, I wrote that “Data + Gut > Gut = Better Decision Making“, and this applies very well to the story telling construct presented above. Theme, Plot, Structure and Characters represent the Data; while Setting, Style and Tone represent the Gut. The data can only tell us so much about the story and point us in the right direction for the business decision at hand. But when coupled with the “Gut” components, representing the business context, the decision maker can be much better informed as to the optimal alternative. A story where we don’t know or understand the setting, style or tone can be confusing or misleading. But by adding these factors, the other elements of the story are grounded and make the story believable and hence helps to build trust in the process and decision…which of course makes for a compelling analytics story.
With most retailers investing in omni-channel capabilities in some way, stockholders, associates and customers are eagerly anticipating the future. A future where there truly exists a seamless bi-directional relationship between retailer and customer (my definition of omni-channel). Where every product in the assortment is available through stores, internet, or mobile and can be shipped in near real time from any location. And every customer activity, purchase, and behavior is known at both the individual and household level.
But will this will be retail omni-channel utopia? Once all of our favorite retailers have these omni-channel capabilities, who will win? Certainly customers will appreciate the ability to purchase and return anything, anytime and anywhere.
But which retailers will win? How can a retailer make their omni-channel initiative a success, differentiate themselves, and achieve competitive advantage? The two keys may be surprising…
1) It is NOT about the product or price…is is about the EXPERIENCE
Customers are both rational and emotional. As a father of two pre-teen daughters, this is a combination of traits that even I might have a hard time grasping. But it is true. The logic side helps them find the best price and quality. It is a fairly clear scenario often with quantitative and definitive answers. And this is where the omni-channel capabilities come in. A retailer either has them or does not. They work smoothly or not. They make the transaction and buying experience easier or they don’t. And the rational customer will research every other competitor to find the best price, lowest shipping, or fastest delivery. And unless a retailer has the scale and resources to take on Amazon, this is going to be difficult.
Those retailers who focus on the emotional side of the customer will have a better chance to succeed. Wow them with service. Create a memory. Do something for them that nobody else can. For at least a short time longer, the one advantage that every bricks-and-mortar retailer has over Amazon is their store. Use it! It is part of your brand and should be at the heart of your experience and customer engagement strategy. Make your store a destination. Give them an experience that they will never forget, an experience that they will want to do again, and an experience that they will tell everyone about!
Define your Customer Experience Strategy…and leverage omni-channel capabilities to bring it to life.
2) It is NOT about omni-channel…it is about ANALYTICS
First let’s get the basics out of the way. Everyone must have standard omni-channel capabilities: buy online return in store; ship from store; buy online pickup in store; and so on. But once most retailers have these, none of them, independently or as a collective, will differentiate or enable competitive advantage. They will simply become table stakes. And if you don’t have them, with few exceptions, you are likely to lose.
For those of you that have approved significant investments in these capabilities…don’t worry – it is not a sunk cost. It is necessary, but not the key. The key is analytics.
There are very few guarantees related to an omni-channel initiative, but I’ll offer two. Fist, the initial implementation will not be perfect. Second, it will generate lots of data. Being flexible and adapting to customers, trends, external factors and more will be critical. Retailers must make decisions quickly and with fact-based data.
Develop an analytics capability now…concurrently with your omni-channel initiative…and you will have the necessary information to make these decisions quickly based on facts. And more importantly, you will likely be 12-18 months ahead of your competitors who choose to focus on analytics only after they have completed their omni-channel initiative.
So take a look at your omni-channel initiative and make sure that you are addressing the customer experience and analytics. Your boss will thank you, your shareholders will thank you, and of course, most importantly, your customers will thank you with repeat purchases and goodwill.
Earlier this week I shared an article with a colleague entitled Can Analytics Helps Colleges Graduate More Students?, by Tanya Roscorla. The article presented some excellent case studies where universities are leveraging analytics to test innovations and help students identify the best path towards course completion and graduation.
While she appreciated the article, I found her response compelling (and obviously the inspiration for this post). She noted that the article stated “Colorado universities send about 20,000 people a year to community colleges because they aren’t ready for college-level math and English. But they’re losing many of these students in the process.”
Her response: “It’s interesting that people see this as a problem with colleges and universities – why are high schools graduating people who aren’t ready for college-level math and English?”
The question is appropriate and indicative of a much larger issue that I won’t discuss here – what are the roles of high schools, lower schools and parents in terms of getting kids ready for college?
But she got me to thinking…it’s great that Texas, Hawaii, Maryland are others are leveraging analytics to help students perform better once on campus…but isn’t that simply working on the symptoms of a larger problem and not really understanding or solving the root cause? And if we don’t solve the root cause, we’ll continue to mitigate the symptoms perpetually.
Let’s think about this in terms of a recent business example. A retailer reported sales at a particular store that did not meet expectations – it missed it’s annual sales target by over 20% (approximately $1M). Initial inquiries looked at the usual suspects…lack of traffic, ineffective marketing, insufficient labor, weather, etc. Sound familiar? And while each of these likely played some role in the missed forecast, after further inspection and questioning, a senior executive found the root cause – the initial forecast was flawed! It used a predictive model based on a different geographic area that was not comparable. So while the retailer could (and should) do everything it can to help improve traffic, marketing, labor, assortment, etc…the reality is that the store was meeting expectations based on the actual geography and demographics.
Both the business and education examples above cause emotions to run high – people are passionate about the topics and have vested interests in the outcome. Roscorla closes her article with this keen insight from Mark Milliron, co-founder and chief learning officer of Civitas Learning:
“One of the most important things to keep in mind when dealing with analytics and interventions is to balance out the hype and the skepticism. True analytics believers promise too much and don’t deliver enough, while skeptics fight back against innovation and change. As a result, true believers and caustic cynics hijack important conversations, which are complicated and require tough mindedness…[organizations] need to calm the caustic cynics, temper the true believers and create a space for learning analytics to move forward to the next level.”
So use analytics to identify symptoms and trends…then ask the 5 Whys (see cartoon below) and use analytics some more to get to the root cause…then make a data-driven decision to solve your problem.
People often have fears, real or perceived, that if not addressed properly can adversely impact the successful implementation of any business transformation. Analytics is no different. Effective change management is a critical component of any Analytics Strategy.
Consider the three fears below recently shared with me and approaches to mitigate them:
- “Analytics won’t produce an answer.” – While this is a viable outcome, it should not be a fear or concern. If the best data scientists analyze the most relevant data with the best tools and methods available…and don’t identify a pattern in the data…that is your answer! The answer may be that the data won’t help – there is no pattern. Your best course of action is to use your instinct and experience. But at least you’ve explored whether the data could help and have ruled that out as a possibility. And you’ll be making a better decision!
- “They won’t tell us that they cannot find an answer.” – This concern goes something like this: the data scientist won’t find a pattern, but in order to justify their rate/job/existence, they’ll fabricate an answer. And while some disreputable analysts might exist, the majority are reputable, inquisitive and curious by nature with a huge appetite to find an answer. They’ll keep digging and analyzing as long as you allow. And when you call it off, they’ll let you know that no pattern was found. So check the references on your hires, be sure you can trust them. Then let them at the data, and be comfortable that they won’t fabricate the result.
- “We’ll think we are smarter than the data and we won’t listen.” – This fear simply comes down to leadership and culture. Does your organization have top down senior level advocacy of a data-driven decision-making culture? And will they hold others accountable? If yes, then this fear is rather easily managed by evaluating your decision making performance. If specific individuals “don’t listen”, consistently decide against the recommendation of the data, and turn out to be wrong, then leadership must hold them accountable. On the other hand, if they turn out to be right most of the time, make sure that you adjust your models, data sets or methods.
Are these fears common in your organization? Likely.
Are there others? Likely.
Find out. Talk to key business leaders and partners, especially those resistant to analytics. Ask them and take time to understand their concerns. Then go about resolving them. Only after ensuring that the people aspect of an analytics initiative is address, can you effectively implement the process and technology changes necessary to implement the strategy.
Over the course of my career, I have authored and reviewed hundreds of business cases. And nearly every one of them included the same qualitative benefit: “better decision making”. For most of them, this was nothing more than filler – an additional bullet point in a list of items that really didn’t matter.
But Analytics is different – better decision making is the entire point of it! How this is achieved can be defined by the simple equation below:
Data + Gut > Gut = Better Decision Making
Let’s start by defining the two variables involved:
- Data – represents the quantitative analysis performed on a relevant set of metrics
- Gut – represents the cumulative experience, functional/industry knowledge and instincts of the decision maker
Now as a decision maker, let’s assume that you have a choice between alternative A and alternative B. Consider the two scenarios below:
- Scenario 1 – Your gut/instinct/experience tells you that alternative A is preferable. Perhaps you’ve seen it be successful more often – maybe you have personal experience in a prior similar situation. Or maybe alternative B has never been tried or proven in your field or industry. Regardless, alternative A just feels like the right decision.
- Scenario 2 – Same as scenario 1, but you also have data to support your decision. After analyzing the appropriate data set, using advanced quantitive methods, your analytics support team advises you that alternative A has a higher probability of a successful outcome than alternative B with a high confidence level.
Scenario 2 clearly represents a approach that will drive a better decision-making process. You’ll have more confidence in your decision, as will your boss, customer, and business partners.
There are two additional questions to think about:
- “What if analyzing the data takes too long or costs too much?” – This is a valid concern an done that the decision maker needs to assess. Is there timeliness or urgency to your decision? How big of a decision is this – a $25k or $25M opportunity? What are the risks of making the wrong decision? In cases where the risk is small, urgency is high, and cost/duration of analysis is also high – then the decision maker should strongly consider going with their gut. But even in this example, you’ve explored the option of using analytics and determined that it was not optimal due to these other factors.
- “What if the data doesn’t show a pattern or indicate a higher probability outcome?” – This is a common fear, but in reality, not a concern. Even if your analytics team comes back and tells you that “there is no pattern in the data” or “we cannot recommend alternative A or B” – this is still information and improves your decision making process. You’ve looked at the data and determined that is will not influence the decision – the probability of success with either option is 50/50. So go with your gut. Knowing this is still better than simply going with your gut and after the fact, wondering if the data might have helped you make a better decision.
But there is a catch – there is no guarantee that alternative A will in fact turn out to be the better choice. It very well may end up being alternative B. Business happens. Unexpected events occur. Surprise variables influence the outcome. Or it is just a matter of probability – sometimes the long shot beats the odds.
Regardless of the actual outcome, A or B, scenario 2 (data + gut) is clearly the better process. You’ve leveraged your gut/instinct/experience and used it effectively and you’ve also leveraged additional information. You’ve been more comprehensive in your assessment. You’ve made a better decision.
Now you can feel good about that bullet point in the analytics business case…and move it over the the list of quantitative benefits (more on that in a future post)!