real-time decisioning

3月 262015

In my ongoing quest to connect people's business problems with sources of technology solutions, my work on the TechnologyAdvice Expert Interview Series puts me in contact with some interesting people behind those solutions. Recently, I caught up with John Balla from SAS and got his insights on how marketing automation relates to mobile customers.

His recent role as a panel moderator and sponsor at the DMA's Marketing Analytics Conference in Chicago put him on my radar screen for the fascinating intersection of mobile with analytics, marketing automation and big data platforms. Here are a few of the highlights from our conversation:

TechnologyAdvice: The session you presented at the Marketing Analytics Conference covered mobile engagement and the differentiating role of analytics. Can you give us some highlights from that presentation?

John Balla at the Marketing Analytics Conference.

John Balla at the Marketing Analytics Conference.

John: When you think about mobile in particular, it's such a powerful platform. It's social, it’s search, and it's basic communications like email, text and phone. So mobile is really something that’s affecting the work of marketers in multiple ways. In the past year, I sponsored two studies on what’s happening in marketing with mobile customers that gave me the chance to zero in on the opportunities for marketers with mobile.

  • The first is a study with the CMO Council that we called “Getting in Sync with the Mobile Customer.” It took a pulse on the enterprise view of what's happening with mobile and their customers, and how marketing departments in these organizations are changing to meet the challenge of mobile.
  • The second study was conducted with Northwestern University’s Kellogg School of Management, where Professor Terri Albert and a group of marketing research graduate students looked at how consumers engage with brands and organizations.

Together, the two studies gave me a chance to see the proverbial “two sides to the coin” and draw some important conclusions about mobile engagement, look at where the linkages are and what some of the trends are that were raised in one project and validated in the other. What we found is that mobile is both a challenge and opportunity in important ways. And while best practices aren’t fully-baked quite yet, mobile is still very much an open field for marketers to establish their market and engage in ways that would give them a competitive edge.

TA: What trends in mobile engagement and analytics can give companies a competitive edge when using marketing automation software?

John: As I mentioned, the biggest problems and the biggest challenges are where the opportunities are. And what I'm seeing is that it's the immediacy aspect of mobile -- that need and desire for all to engage in real time -- is where the opportunity lies.

So let's look at a very well talked-about subtopic here: showrooming. 

Showrooming is rooted in the idea that your customers are engaging with your brand online, offline and especially simultaneously. It happens while they're in your store, they’re using your WiFi and looking at your merchandise, but then they’re also looking at what other stores -- your competitors -- are offering. For retailers in particular, that's a big problem, however, it also creates opportunities.

If you're able to engage your customers with a real-time decision engine tied to your automation platform [while they’re in your store], you have an idea of what they want. You can engage them actively knowing what they’re looking at and possibly why, and you increase the chances of keeping that sale in-store or in-house via however you reached their mobile device.

And this is a classic big data issue - it’s mostly driven by customer data. The choice is yours to either respond to it as an opportunity, or see it as a challenge that needs to be managed.

TA: Right, and your competition, they're taking advantage of the opportunities. So it's not only that you're missing the opportunity, but you're falling behind. 

John: Exactly. Then it's a business problem, not so much an IT problem. With mobile changing the way people behave, they really want to hear about what you have to say and what you might offer them when they happen to be looking for it at that moment. But it has to be on their terms - you can’t go around bothering them. It's like the whole idea of the “Do Not Call List,” right? When you have people getting annoyed by getting these phone calls during dinner, then they don't want to talk you about whatever you're selling because they’re busy with something more immediate and (to them) more important. 

It's the same way with mobile. That's why having a robust, real-time decision engine tied to your automation platform is going to help you achieve that relevancy where you're not annoying. I think everybody is still trying to figure how to do that and not be creepy at the same time, though. But that [the creep factor] is going to be something everyone -- marketers, customers, society -- are going to have to get used to and come to terms with.

 TA: With access to all this data, how can businesses find that balance in their marketing automation— of giving the customer relevant and meaningful information that they want — without being creepy about it?

John: It’s the whole idea of how to use big data in a way that's responsible. Using opt-ins, safeguarding data and respecting privacy are key. The relevancy  factor is particularly important because it affects customers and it affects how they perceive you and engage with you. Big data is largely customer data. It's the transaction, it's the engagement and the bread crumbs left across your website. That's where the value is. 

If you’re able to combine it, analyze it, and get the insights and the understanding, then it enables you to be more informed and more educated and appropriate in how you engage with your customer.

It all comes back to marketing automation because when it works well, that is to going to be the engine that controls your engagement with your customers. That's the beauty of analytics. It gives you the ability to combine all this data and use it meaningfully. It's going to give you the ability to drive the insights that make you more effective as a marketer.

To learn more about Marketing Automation Software, big data platforms and CRM, visit

Listen to the entire show above in order to hear our full conversation, or click here to listen later. You can subscribe to the TA Expert Interview Series via Soundcloud, in order to get alerts about new episodes. You can also subscribe to just the Marketing Automation category. 

The podcast was created and published by TechnologyAdvice. Interview conducted by Clark Buckner.

tags: big data, DMA, marketing automation, mobile, real-time decisioning, TechnologyAdvice

The post Interview: How marketing in the moment matters for mobile appeared first on Customer Analytics.

3月 062015

Financial goals are best reached with long-term planning - be it savings plans, carefully arranged financing, or any and all forms of insurance coverage.  When it's part of a plan and all goes well, the results intended are the ones achieved. All very good, right?

A real crunch almost always calls for a real-time response.

A real crunch almost always calls for a real-time response.

But even the best laid plans at some point go awry. Or things simply happen ("it happens') - it's a part of life.

Those can be known as "crunch times," especially when the solution is a financially-driven one that calls for getting a short-term loan. That crunch time can be caused by an accident, a natural disaster, or even a great opportunity that can't be passed up, and in that moment the last thing the customer wants to hear is that you can't help them.

That's the scenario that plays out for most customers of Advance America, a leading provider of short-term loans for people in situations not normally served by banks or other traditional lenders. They can be payday loans, online loans, installment loans, title loans and more. The one critical common denominator to all those scenarios is that they call for real-time responses. So in order to keep their business viable, Advance America needed to find a way to respond effectively in real-time to their customers' crunch times.

In order to do that, Advance America uses analytically-driven real-time decisioning with the goal of being consistently fair, thorough, and most of all, fast. On any given day, they process approximately 20,000 decisions and average about 15-20 milliseconds per decision. They also process about 1,300 customers per day and average about 3 seconds per decision.

They pair that real-time responsiveness with a sensitivity to the needs of the customer that delivers a stair-stepped process for delivering greater benefits for the best customer experience. The company knows that "no" is not a good answer to a request for a short-term loan, so the customer may be offered a smaller amount than originally requested.

That approach fosters trust in ways that builds a long-term relationship because over time and through a good repayment history, the customer reduces his or her risk profile.

Through this approach, 97% of Advance American customers rate their overall experience with the company as good to excellent. Does that increase the chances that they'll reach out to Advance America again next time their in a crunch? I think we can be quite certain that they will.

You can read more about the  Advance America story in Improving Customer Experience through Real-Time Marketing.

You can also hear details in real-time at The DMA's Marketing Analytics Conference in Chicago, March 9-11, 2015. John Lodmell, VP of Credit and Data at Advance America will present the story of real-time decisioning and how it drives customer experiences that build long-term loyalty.

tags: customer experience, DMA, real-time decisioning
2月 232015

Many retailers do not engage in effective forecasting.Retailers are always trying to get closer to customers. But it’s not just about improving service to those customers – it’s about understand more about what products they are demanding so as to make better forecasting decisions around, for example, how much of a particular item is needed in stock.

As this video reminded me, knowing your customers really well means you can easily know what products they will want, when and how, perhaps better than they do. Today, that knowledge is gleaned from a complex assessment of lots of online and offline data which contributes to every single buying decision by your customers.

The online element means it’s not just about attracting customers into the store. Visiting New York for the annual retail conference NRF a few weeks ago, I thought about how the value of retail space has changed. Retail giants that have been there for years, as well as the new retailers on the block, are continuing to transform the experience in-store to stay ahead of customer demand. We are enticed in-store with Wi-Fi, coffee, beer, or live DJs. But this only gets a retailer so far if they cannot capture key data about customers coming in to their stores.

The main problem, however, is not one of getting hold of data; it’s being able to forecast using that data.

We recently conducted the 3rd Annual Analytics in Retail Study, which reports that 71 per cent of retailers performed either basic or no reporting at all when it came to forecasting customer trends. Whilst a large proportion had the ability to gather data, and many were doing so, there is a clear gap in ability to analyse this data to inform business strategy. There are many possible reasons for this; a lack of in-house skills, and/or a lack of awareness of available technology that is more accessible.

At NRF it seemed clear to me that most retailers are continuing to find forecasting a problem, as they are also struggling against the volatility of promotions on stock and the lack of good data around non-traditional channels. As the view of the high street continues to change, shifting that ability to forecast for future buying trends will be the next frontier for retailers in 2015.

More and more retailers are looking at ways of capturing customer data in-store. Apple CEO Tim Cook recently declared 2015 to be the year of Apple Pay, which already makes up more than $2 out of $3 spent on purchases using contactless payment across the three major US card networks. We heard at NRF that this will be a focus for some of the UK's largest retailers. It provides an excellent opportunity to engage customers and enable them to pre-order items. Applying analytics to this data should help develop deeper customer relationships, and more personalised offers and prices. Crucially it also offers insight into customer demand so retailers can make better forecasting decisions.

You may also be interested in my colleague Alison Bolen’s take on the three big trends to come out of NRF this year. Read more about those here. In the meantime, let me know what you think. And thank you for following!

tags: #NRF15, Campaign Management, Inbound marketing, marketing optimization, real-time decisioning, retail
12月 292014

Just imagine the data streaming from each connected car.

Decades of Westernized television and cinema have featured fantastic imagined car technologies, including many that now actually exist. Think of the autonomous car from Knight Rider and today’s self-parking capabilities. Or the ongoing James Bond series with tracking devices that resemble the well-established GPS systems in our cars, phones and even wearable technology.

Art has become reality, and it continues to evolve for automotive manufacturers. Why? The short answer is that we’re obsessed with innovation. And while the connected car concept isn’t exactly new, the sheer breadth of the concept certainly raises new questions. Consider this thought in light of the opportunity made possible by analytics:

Machine-to-machine (M2M) connections
are expected to reach an estimated
1.8 billion worldwide connections by 2022. 

At a recent Connected Vehicle Trade Association (CVTA) summit, the reality of automotive connectivity and associated big data challenges were explored. If an estimated one terabyte of data per hour is generated, according to Andreas Mai, Director of Smart Connected Vehicles for Cisco Systems, consider the opportunities to transform the automotive customer experience.

Some automotive customers use SAS’ Advanced Analytics capabilities with real-time decision making for credit scoring, enabling smoother customer experiences at a critical juncture - during financing negotiations. Others use Predictive Analytics for generating real-time offers at the point of service based on customer feedback, past purchase, service data and other interactions associated with the brand.

However, the connected car revs up a whole new world of customer interaction. Picture better driver safety capabilities by predicting recall and accident-related potential. Or the deployment of SAS® Quality Analytic Suite, currently used by Volvo Truck, could expand to include the use of streaming data for minimizing downtime due to breakdowns.

Envision location-based services that automatize the vehicle to get around traffic or inclement weather, or go to a fuel station when a minimum threshold is reached. Personalize the driving experience with media content using connected car infotainment options based on streaming data. The data collected by SAS customer Maruit-Suzuki could expand into real-time streaming of preferences for even further insight.

So how do we get to all this streaming data? Managing data in motion (real-time, not the moving vehicle), is quite different than data at rest. The SAS® Event Stream Processing Engine, often referred to as ESP, offers access and relies on three principal capabilities – aggregation, correlation and temporal analytics.

  1. Aggregation. Let’s say you wanted to detect the costs of repairs per vehicle: “Tell me when the value of repairs on any day is more than $x per day (or even hour)”. Rather than waiting for aggregated metrics to tell you if your vehicles are causing repair costs higher than annual averages (impacting poor customer experience potentially leading to defection), you could have a real-time pulse of repair counts.ESP can continuously calculate metrics across sliding time windows of moving data to understand real-time trends. This kind of continuous aggregation would be difficult with traditional tools.
  2. Correlation. Connect to multiple streams of data in motion and, over a period of time that could be seconds or days, identify that condition A was followed by condition B, then condition C.For example, if we connect to streams of diagnostic codes from thousands or even millions of vehicles on the road, ESP could continuously identify conditions that compare vehicle service events to each other. This might look something like “Generate an alert if a certain diagnostic code is more than 150 % of the average of other vehicles.”
  3. Temporal analysis. ESP is designed for the concept of using time as a primary computing element, which is critical for scenarios where the rate and momentum of change matters. Take surges of activity as clues to potential quality issues as an example, where ESP could detect such surges as they occur.Consider searching for parameters such as: “If the number of diagnostic codes triggered within four hours is greater than the average number of daily DTC triggers of that vehicle / make / model in the previous week, launch an immediate audit of that part’s quality.” Unlike computing models designed to summarize and roll up historical data, event stream processing asks and answers these questions on data as it changes.

Improved automotive products and customer experiences are the end goal for the connected car. But possibilities go on for miles, or kilometers if you will. It’s an exciting time in the world of automotive big data analytics. While futuristic transportation ideas like the 1960s Jetsons’ flying car may not exist yet, it’s just a matter of time before we’re all talking, or perhaps texting, with our cars.

For now, start with what’s right around the corner for the connected car. Take a look at IIA’s report Making Meaningful Predictions in the Fast Lane for a rundown on a few of the many possibilities for event stream processing, data sensors, data in motion and the Internet of Things.

tags: analytics, big data, connected cars, customer experience, event stream processing, Internet of Things, real-time decisioning
12月 232014

When we talk about digital customer services, it’s all about creating online self-service capabilities for our customers. This is great for everyone! We’ve made it easy for the customer to get what they need; it’s cheaper and faster for organizations to deliver the service to the customer. Need a copy of your receipt? Want to know where your package is? No problem! Over the past decade, many companies have moved to self-service models that promote convenience, accuracy and speed for most normal, everyday transactions. But what if you don’t have a normal, everyday problem? Companies are taking three basic approaches to digital service strategies:

No Humans Available

How hard is it for your customers to get service?

How hard is it for your customers to get service?

Many of the big e-companies won’t even post a contact number on their site (or at least put it in an obvious place). Just try and find a human to talk to at Facebook, eBay, Twitter or Uber, for example. Most of these sites will first route you to the FAQ board (they want to help you help yourself), the community board (get other community members to help you), push you through the digital equivalent of a call center IVR (press 1 for this, press 2 for this, etc.). You get to the end of what seems like an infinite decision tree of questions and you still didn’t get the answer you were looking for. And if none of that works, maybe – just maybe – you’ll be allowed to submit an email to “customer support.”

Okay, this isn’t all bad – after all, people do ask a lot of dumb questions, but most online “Help Centers” aren’t very helpful if you don’t know how to ask the right question in the first place. Use at your own risk.

Unhappy Humans as a Plan B

Are your service reps THIS cheerful?

Are your service reps THIS cheerful?

I just went through this process with a newspaper I subscribe to. Just last week they served me with an account cancellation notice. The credit card tied to the account had expired four months earlier. I received no notifications that the card had expired, and they let me run up a tab before sending the cancellation notice.

Four things needed to be fixed: update credit card, pay outstanding balance, reinstate account, and continue service. Only one of these transactions could be completed on the web. I also discovered that home delivery and online subscription services were separate. I first attempted to fix all of my problems online, which turned out to be impossible. I caved and called customer service. After navigating an irrelevant IVR menu, I spent 15 minutes on hold before reaching a surly customer service representative. Everything got fixed, but really? Are my expectations too high?

Self-Service with Real Humans

Isn't it so much nicer when they're both cheery AND helpful?

Isn't it so much nicer when they're both cheery AND helpful?

One of my favorite examples of an organization that does this well is Progressive Insurance. Their online auto insurance quoting process takes about five minutes. As you go through the process, their rate-quote engine makes calls out to external data sources and predictive pricing models in real-time. If you have questions at any time during the quote, you can click-to-chat or call a customer agent. Your online information is saved in-session and the customer service reps can see where you are in the quote process without making you repeat any information. They’re there if you need them, but not if you don’t (and they’re pretty darn cheerful if you need assistance).

The reality today is that most businesses are technology driven, so digital services must become part of a company’s DNA. Digital service design is all about understanding what services the customer wants and then enabling technology to allow the customer to complete that service. It’s not about disintermediating humans from the process, but using technology to facilitate positive and profitable customer interactions.

And if your customers are not getting the service they want, they might just air their grievances on social media: #customerservicefail.

tags: customer service, Digital, real-time decisioning, social media analytics
12月 182014

Black Friday is the fourth Friday in the month of November.Few of us can have missed the scenes of frantic shoppers searching for that ultimate bargain on Black Friday. This is something fairly new in the UK, having originated in the United States to refer to the Friday following Thanksgiving Day. Legend has it that ‘black’ refers to the first day of the year that retailers operating on squeezed margins would go into profit, leaving the 'red' of loss and into ‘the black’. Black Friday really took off in the US around 2003 when it became the busiest shopping day of the year; and in the UK a few years later. And now Black Friday is followed by ‘Cyber Monday,’ when bargain hunters hit the net in search of the best deal.

In fact, according to The Guardian, Black Friday bargain hunters helped to lift UK retailers out of their usual November lull. Online spending went up by 37.5% from last year, while total retail sales were up 2.2% against same time in 2013.

Two new surveys shed light on the rapidly changing behaviour of consumers on both sides of the Atlantic:

Examining Christmas Shopping Patterns in the UK

The UK report with Conlumino looked at how effective discounting is, what factors affect forecasting, how relatively new phenomena such as widespread online retailing and ‘Black Friday’ are influencing behaviour?

CyberMondayMaureen Hinton, Global Research Director, Conlumino, said: “Having been first introduced to the UK market by Amazon just a few years ago, Black Friday and Cyber Monday have been increasingly embraced by UK shoppers. Americans traditionally view the Thanksgiving weekend as the start of the Christmas shopping season and – whilst UK shoppers don’t share that connection – the pro-activity of UK retailers with their heavy marketing and deep discounts, allied to the rising influence of e-retail, has played well to an enduring sense of austerity. According to our research, just under a fifth of shoppers have long planned to purchase Christmas gifts online on Cyber Monday as part of their Christmas shopping cycle, highlighting that the success of the weekend is down to a strong cocktail of impulse-led and planned purchasing behaviour.”

However, our research with Conlumino also showed that some consumers may regret their impulse buys; with almost three in four saying that online purchases should be allowed to be returned in store. We could term this pattern ‘Return & Run’ where shoppers simply return unwanted items or ‘Shop & Drop’ where they make new purchases at the same time as returning unwanted goods.

“Nonetheless”, adds Maureen Hinton “over two-thirds of UK shoppers feel that Christmas discounting is losing its impact, which is hardly surprising given that the last few years have seen retailers engaging in more and more promotional activity throughout the year. Against this backdrop, it will take deeper levels of discounting on the products and brands that shoppers actually want to effectively drive demand; over half of consumers are planning to shop around for the lowest price once they’ve picked their Christmas gifts. The weather is also a major concern for retailers … with 69% of large retailers saying that it has a medium-to-high influence on performance.”

XmasDiscountsWith shoppers becoming ever more unpredictable in their buying and returns behaviour, Black Friday no longer guarantees a healthy profit for retailers, even with the appeal of bargains, same day deliveries and ‘Click & Collect’ services.

Matching product promotions to different consumers is very complicated, but the good news is that data analysis provides the answers. Unfortunately, 44% of retailers said they still rely on gut feel for forecasting demand, and nearly half (47%) said they promote the same goods every year. Big data analytics extracts key insights from the data so retailers can accurately forecast demand for different products among different consumers, and ultimately deliver consumer satisfaction.

Searching for the ‘Average’ Shopper in the US

Meanwhile, over the Atlantic the search for the typical shopper was taking place. The US survey report details a view taken from the consumer’s perspective.  We found that she’s almost 46 years old, she shops for 13 people and plans to spend $1,119 (£720) on gifts. She will also indulge her significant other to the tune of $299 (£190).

The survey revealed seven dominant holiday shopping styles: the Black Friday Warrior, the Budget Buster, the Practical Shopper, the Perfect Gifter, the Cybershopper, the Last-Minute Hopeful and the Humbug (listed in descending order based on the average amount spent on Christmas gifts):

  • Black Friday Warriors (21%, average spend $1,422/£923) enjoys going to the shops and being part of the crowd. Has the longest gift list.
  • Budget Busters (11%, average spend $1,132/£724) generally end up over-spending, don’t mind paying more for convenience.
  • Practical Shoppers (21%, spends $1,108/£709) saves, sticks to their budget and does all of their shopping at once.
  • Perfect Gifters (19%, spends $1,056/£676) spends time searching for the perfect gift, loves to indulge family and friends.
  • Cybershoppers (19%, spends $955/£611) finds shopping stressful and is not inclined to take advantage of the Black Friday deals.
  • Last-Minute Hopefuls (5%, spends $955/£611) the least stressed group, they leave it all until Christmas Eve, picking up bargains and gift cards.
  • Humbugs (5%, spends $941/£602) are the opposite of Black Friday Warriors. They think Christmas decorations appear too soon, dislike crowds, don’t indulge loved ones and won’t pay for convenience. They are stingy, delay until the last minute and shun sales.

Alan Lipson, SAS Global Retail Industry Strategist said: “Our research suggests retailers should target the budget-conscious shopper during the Black Friday weekend. Alternate promotions are in order as the season progresses. Effectively appealing to different consumer segments – especially during the holiday season, when consumers aren’t shopping for themselves – is a complex puzzle. Analysing data is the only way to solve it.”

For a slightly different take on these findings, read this blog post written as a poem by Pamela Prentice titled, How many holiday shoppers will brave the crowds on Thanksgiving? Pamela, SAS's Chief Research Officer, is a talented writer and is apparently a budding poet as well.

Two Markets with Similar Challenges/Opportunities

On both sides of the Atlantic, the way consumers behave has changed, as have their purchasing patterns, how they react to discounts and promotions and how they shop both on and off line. Making sense of these changes to manage the challenges and capitalise on the opportunities is increasingly difficult. That's where the differentiating power of analytics can make a difference.

Retailers of all sizes are using marketing analytics to gain a fuller view of customers from online behaviours, social media sentiment, transactional data and other sources of data. Armed with that data, they are using real-time decisioning to inform customer interactions both online and in stores. These technologies better equip retailers to deliver customer experiences that make a difference on Black Friday, throughout the holiday season and all year long.

tags: marketing analytics, real-time decisioning, research, retail, social media analytics
11月 212014

Customer experience matters most for loyalty.It seems like every retailer nowadays has a loyalty program. From the local coffee house to “big box” national retailers to almost any online merchant, everyone has a loyalty program. But do people really want them? It turns out the answer is yes – a resounding yes.  But are those programs actually what drive loyalty, or is something else driving behavior?

That question loomed on my mind this year as I had the opportunity to work with two organizations to research customer loyalty - one project focused on the enterprise view, and other to get the consumer view. For the consumer view, I had the privilege to work with graduate students from Northwestern University’s Kellogg School of Management, and that study confirmed a few suspicions I had about customer loyalty programs. And it’s a combination of good news / bad news.

The Bad News
Let’s start with the bad news and get it out of the way. It seems that consumers have come to expect loyalty programs, so in many situations not having one may put you at a disadvantage if your direct competitors have such programs. The other bad news is that so many loyalty programs are tied to discounts and have been aggressively promoted as such that it’s the benefit that consumers associate most strongly with loyalty programs. So, the upshot is that with a loyalty program in place you’ll need to figure out how to operate on slimmer margins, or make other accommodations.

The other bad news is that loyalty programs designed to keep the customer coming back do little to trump a bad customer experience. As a result, retailers must first ensure that they are delivering quality shopping experiences before offering perks for return trips. So as they are designed today, do loyalty programs actually engender loyalty? Apparently not, according to the Kellogg study – it’s a combination of factors.

The Good News
It’s not all doom-and-gloom, however. The good news is that the combination of factors to drive loyalty have many elements that are within your control. And which factors matter depends on the type of retailer you are and what your customer base looks like. Do you emphasize convenience? Low prices? Personalized service? Are your customers “necessity shoppers,” “practical shoppers,” or “pleasure shoppers?” All those factors emerged  in the research and are explored in the report.

But one clear conclusion from the study was that experience matters most – so delivering a good customer experience should be your primary focus through all interactions with your customers (including your loyalty program).  Then from there, it’s a question of reviewing your customer-facing processes and aligning internally so what your customers experience are both consistent and in line with what’s expected. And unless you are a mom-and-pop shop with a limited selection, you’ll need more than a spreadsheet to understand what your customers expect and how you’re delivering on your brand promise.

More good news
The proven way to get a full view of your customers across all channels no matter where they are in their purchase journey is to use marketing analytics. Those same analytically-driven marketing solutions can also be used to orchestrate your customer interactions across your organization and to inform them in real-time as is often the requirement. Want proof? Read these use cases on Staples, Macy’sOberweis Dairy and the luxury goods flash-sale retailer Gilt.

Or you can start by downloading the Kellogg School’s report, Shopper Insights to Improve Retail Loyalty Programs. Another option is to tune in to a December 2, 2015 webinar produced by Loyalty 360, which will highlight the findings of the Kellogg School study and the parallel enterprise-focused study we sponsored this year with the International Institute of Analytics.

And as always – thank you for following!

tags: customer loyalty, customer retention, Kellogg School of Management, marketing analytics, marketing automation, real-time decisioning
11月 192014

The higher the revenue potential, the more interesting the idea.Back in 2007, the NY Times published an article about “The Google Way.” The premise behind the Google Way is to give engineers 20% of their time to spend on new company related ideas and projects that interest them. For a while this became the management strategy du jour as every large company attempted to inject a culture of spirit and creativity into their businesses (with very mixed results).

Even Google recognized that the strategy was flawed. Large initiatives generated from The Google Way projects begin to distract them from their core business.

Taking the reins as CEO in 2011, Larry Page announced a new focus with “more wood behind fewer arrows.” Google put guardrails on how innovation time is spent: “Urgency without alignment is wasted energy.

Marketing organizations have taken a page from the Google playbook. In my previous post, I noted the disparity in organizations with marketing innovation budgets (who has them, who doesn’t), and highlighted some examples of organizations putting their innovation dollars to good use. But how do we validate and prioritize those innovation ideas, align them to our marketing goals, and execute?

Lowe’s home improvement stores is getting ready to release a sales-robot to help their customers navigate their stores more easily. The robot is a brainchild of the company’s Innovation Lab, a technology company-within-a-company. The Innovation Lab uses a technique they call “science-fiction prototyping.” Team members include profession science fiction writers! Next item in their innovation funnel is a “holoroom” that will allow customers to simulate renovation projects. Lowe’s is betting on a future where people interact with in-store and virtual technology in new ways.

This summer, Mondelez International, the maker of Oreo cookies, created a promotional campaign for their new mini-Oreo by sending one tiny cookie per household to people living in small US cities. The campaign backfired. As one newspaper columnist in Great Falls, Montana noted: It’s like [Oreo] said, “We’re sorry you have to live in the middle of nowhere – here, have a cookie.”

While this campaign caused a PR headache, it’s obviously not a catastrophic fail for the company. I’m sure people in small towns will continue to eat Oreos. Mondelez missed the mark on understanding their target audience. Innovation must be aligned with customer insight: The data has to support to the customer, not the organization. Kraft’s social learning lab – called The Looking Glass - helps the company understand “consumers at the speed of culture.” A team of people analyze digital consumer behavior (both internal and external) and use that information to inform product and marketing decisions.

As you’re starting or expanding your marketing innovation capabilities, data-driven decisioning will shape your innovation pipeline. By analyzing customer information, you can start to weed out the good ideas from the bad. After all, who just wants one cookie??

And as customer expectations for real-time interactions increase, your need for real-time decisioning will increase. Take a closer look at how well real-time decisioning can integrate into your overall marketing scheme. You'll know every time how many cookies are expected, and also which flavor and whether they want milk with it. Let me know what you think.

tags: best practices, innovation, real-time decisioning
9月 182014

Sunset over Marshall Field's Chicago, circa 2014

Sunset over Marshall Field's Chicago, circa 2014
[photo credit: Barry Butler Photography]

"Give the lady what she wants" and "The customer is always right" are quotes attributed to the venerable Chicago retailing pioneer Marshall Field. That customer-centered approach to doing business was leading-edge at the close of the 19th century and soon became a competitive advantage for Mr. Field's namesake department store empire.

Fast-forward more than a century, and it turns out that same customer-centered approach remains a best practice for marketing, but what's different is the operating environment. And in some ways, being customer centric is at once more challenging and more achievable than it was in the 1890s. The key lies in using marketing analytics.

In today's market, there are very few situations where one-size-fits-all marketing is the best approach. Customers are empowered, and unhappy customers will flock to competitors, often taking their friends with them. As a result, organizations can't afford to forgo efforts to know their customers as well as possible. And it's the companies that can quickly turn data into intelligence that have a leg up in the race to attract and retain their customers.

A recent TDWI checklist report, Applying Analytics with Big Data for Customer Intelligence, does a nice job of highlighting seven benefits from using marketing analytics and providing compelling examples of how those benefits play out in practice:

  1.  Gain a full view of customers across channels.
    Considering the explosion of channels facing marketers today, this benefit can't be overstated - bricks-and-mortar locations, kiosks, call centers mobile, partners, e-commerce, social media and more. A key first step is to develop a strategy for accessing and integrating customer data and then analyzing multiple sources.
  2. Become more proactive and effective.
    Marketing analytics can help organizations anticipate customer and market behavior and respond proactively. Marketing analytics includes solutions that perform statistical or data mining methods that enable you to develop and score predictive models based on combinations of variables.
  3. Personalize your marketing and customer engagements
    Marketing analytics enable organizations to explore how customers in defined segments behave differently, and even predict customers’ likelihood to respond to different offers. Doing that enables them to tailor the timing, content and delivery channel of offers to fit the preferences of customers, or to channel Marshall Field and "give the lady what she wants."
  4. Sharpen social media strategies
    This goes hand-in-hand with personalizing customer engagements, with the added benefit of getting an ongoing 24x7 focus group of sorts. By using social media analytics, organizations get a candid external perspective, can identify influencers, and can complete the picture of customer value.
  5. Engage your customers in real-time
    Combining speed with intelligence can provide a competitive advantage in-person, on line or on the phone. Real-time decisioning puts all channels on the same playing field enable the same care as the smartly-dressed attentive Marshall Fields' sales clerks of yore.
  6. Visualize success across the enterprise
    Data visualization brings analytics within reach of non-technical marketers, and therefore enables sharing, collaboration and decision-making to collectively become more efficient and effective.
  7. Treat data as a strategic asset
    If customer data weren't valuable, we wouldn't have hackers continually breaking in and stealing it. But locking it up in a vault is also not the answer. A balance has to be struck between data access for analytics and privacy and governance to safeguard the interests of both the organization and the customers.

Applying Analytics with Big Data for Customer Intelligence has much more detail than what I've summarized here, and it's worth the effort to download and read it.

Check it out and let me know what you think. As always, thank you for following!

tags: data visualization, marketing analytics, real-time decisioning, social media analytics, TDWI
9月 052014

Déjà vu. For me, this term immediately conjures images of Bill Murray waking up in Punxsutawney, Pennsylvania on Groundhog Day – repeatedly.  In French, déjà vu means “already seen” and while I usually fall solidly into the realm of skeptic in matters like these, I have to admit feeling a strong sense of déjà vu these days.  Here’s why.

After spending the last six plus years focusing primarily on data integration consulting, I have recently changed my focus back to customer intelligence.  Discussions with our clients who are practicing customer experience management (CEM) today presents an interesting coincidence, one which causes me to feel like Murray on Groundhog Day.  Except instead of re-living yesterday, I am transported back to mid-2000.

Take CEM.  At its simplest, CEM is the ability to influence, monitor, and improve every interaction the customer has with the organization.  This definition evokes conversations with past clients about how and why to migrate from “managing the mailbox” to “controlling the communications.”  Back then we were beating the drums, warning clients that their customers would grow to expect this, attempting to shake marketers (and other business lines) out of their silo-ed complacency.

A case in point is an article I wrote for DMReview magazine in April of 2008, titled, “How Do I Love You – Let Me Count the Ways.”  In it, I posed the question “How do you avoid giving your customers too much love?” and I advocated coordinating and prioritizing customer communications across touch points. I urged readers to avoid over-communicating with their customers.  And in a humorous bit of prescience, I highlighted that “this is not science fiction” but rather an exceptionally effective way to facilitate a seamless dialog with customers that enables a positive experience.

Déjà vu? Yes and no. Marketing managers have always wanted to manage the customer experience. But the developments since that 2008 article have been dramatic. Advanced analytics tools such as customer experience analytics, propensity models and lifetime value models have come into their own.  Capability-rich marketing technologies such as real-time decision managers, customer experience personalization, offer optimization, and digital marketing solutions have entered the picture.  And marketing has become the glue that binds silo-ed organizations together—this is the subject of my next blog post—and advances CEM out of the concept stage and into reality. If this is truly déjà vu, I, for one, am thrilled to be re-living this particular moment again.

If you’d like to delve a little deeper about CEM, check out this recent report by HBR published based on research in this area called “Lessons from the Leading Edge of Customer Experience Management.” Let me know what you think.

tags: customer intelligence, digital marketing, hbr, marketing optimization, real-time decisioning