What is context? According to Merriam-Webster, context is “the situation in which something happens or the group of conditions that exist where and when something happens.” We’ve been using it since late Middle English speakers adapted the Latin contextus, from con (together) and texere (to weave). For marketers, context means understanding attitudes, behaviors, and preferences to address the requirements of individual customers in their moments of need.
It is critical for marketers to embrace customer context. Why? Winning in the age of the customer depends on the interactions that people have with your brand, and compelling customer experiences materialize only when your firm understands its customers and anticipates their needs. The context of all those interactions determines whether customers will engage and, more importantly, transact with your brand again. Marketing’s job is to harness the power of customer context to create a repeatable cycle of interactions, drive deeper engagement, and learn more about the customer in the process.
Smart cities are a myth. But cities are now finally ready to invest in new technology. No, I don’t find those two sentences contradictory. Yes, I do finally feel like the hype of smart cities is fading. And, yes, I do think the promise still holds much potential for cities. But boy have I tired of hearing smart, smart, smart, smart, smart (somehow 5 times sounded right to me, or should I say sounded “smart”).
Back in 2010 I wrote a lengthy report on the smart city opportunity for vendors. At the time my research was focused on vendors, and as the vendors were all worked up about smart cities it made sense to put some structure around the opportunity. What were the primary market drivers? What issues were cities currently facing or expecting to face in the future? Anyone who has attended a talk on smart cities knows the drill ad naseam: population explosion, urbanization, startling impact on city services (transportation, waste and water management, public safety, health, education etc.) And, I’m just as guilty. The slide at the right was from my first webinar on smart cities in 2010.
Recently, in The New Yorker, Mary Powell, CEO of Green Mountain Power, a small energy company in Vermont, told a story of customer-obsession. Her customer-obsession starts simply: Help customers reduce their energy footprint at no net cost. Green Mountain accomplishes this by investing hugely in the latest and best technology, to pull electricity from the sun, insulate the bejesus out of the house, run massively efficient heat pumps, and micro-manage the draw on the power grid draw. Yes, the capital expenses and labor costs are immense. But when you reduce a home's energy footprint by 85%, you reduce the $250 electric bill by 85% -- or more than $25,000 over 10 years.
Green Mountain Power has a customer-obsessed culture and a customer-obsessed operating model. But it also has become expert in using technology to win, serve, and retain customers. The company is technology-obsessed, often out ahead of even the pundits when it comes to the latest technology. Green Mountain Power unites all three forces to be customer-obsessed: culture, operating model, technology.
The same is true for every company and government. Igniting a culture of customer experience is important. Relentlessly improving the operating model to put customers first is also important. But without the right customer-serving business technology in place, customers will be stuck with ancient web sites, cranky mobile apps, pathetic call centers, and disempowered employees.
Gone are the days when the only signal a streetlight sent out was that it was time to go home on a summer evening. Many kids grew up with that rule. My mom had a cowbell, which was infinitely more embarrassing but likely more effective in calling us home. But times have changed. We now text our kids to get them home for dinner. And, street lights themselves would no longer deign to serve just that purpose.
Streetlights these days do provide light (and do that much more efficiently), but they just might be your source of Wi-Fi or of information on the weather, air quality, traffic, and parking availability, or might be the city’s source of information on you. They will also be a platform for new services that leverage all of the data the new light poles collect through their embedded sensors, or also a source of electricity to power digital signs through solar-energy. These new and improved streetlights are becoming increasingly popular as they demonstrate a clear cost-savings over their predecessors and promise the potential for revenue generation through new applications and services. That is a win-win for cities, citizens and the ecosystem of potential application and service providers out there.
We all know that empowered customers expect brands to deliver contextually relevant experiences based on their individual preferences for content, timing, location, and channel(s). How do customer insights (CI) professionals decide the appropriate course of action – not just for a single customer, but for all customers? How do they then execute on those decisions and measure the impact? Systems of engagement like Real-Time Interaction Management (RTIM) provide answers.
Forrester defines RTIM as: Enterprise marketing technology that delivers contextually relevant experiences, value, and utility at the appropriate moment in the customer life cycle via preferred customer touchpoints. In my latest brief “Demystifying Real-Time Interaction Management,” I explore evolving RTIM requirements.
When computers were invented 60 years ago, nobody would have thought that gazillions of 0 and 1s would soon rule the world. After all, that’s all there is in any computer memory, be it a laptop, a mobile phone, or a supercomputer like Watson; if you could open memory up and visualize the smallest elementary unit, you would “see” only an infinite sequence of 0s and 1s, something that would look like this:
Interestingly, that has not changed. Computers are still processing 1s and 0s. What has changed is that we live in an age of digital disruption, an age where software applications run and rule our business more and more. To be successful, those applications need to be engaging and entertaining so that consumers enjoy and are delighted by them; they also have to be mobile and accessible anywhere and at anytime, and they have to leverage tons of information, no matter if it comes from a database, a tweet, or Facebook.
Last year, we saw mobile apps getting smarter, tapping a wider range of personal data to anticipate and deliver in-the-moment needs before a customer takes action. Google is in the lead with Google Now, but Apple and Microsoft also signal interest in this space. Much like the VIP concierge services of major credit cards and airlines, these apps have the potential to form intimate customer relationships and increase affinity for products and services. And they are resetting expectations in a new paradigm we call the mobile mind shift — the increasing expectation of individuals that they can access any service, in context, in their moments of need.
You have an opportunity to play in the game, but to a different tune, one that enriches your brand by enhancing existing scenarios, engagement points, and relationships.
In 2014 and 2015, we anticipate that customer-obsessed companies in verticals such as retail, finance, and insurance will introduce and develop proactive features in their mobile loyalty apps. CIOs should expect an influx of requirements from marketing peers leading such efforts. With the opportunities will come challenges on three dimensions:
1. Business strategy. Proactive experiences can reap extraodinary rewards but can also lead to devastating consequences. For example, achieving 85% accuracy with your recommendation engine appears to be a success — until you consider the diminishing returns of a 5x penalty on trust factor for that 15% you got wrong.
I’ve been experimenting for the past year or so with several proactive assistant apps to guide my day — they remind me to get on conference calls with clients, offer to text participants if I'm running late to an in-person lunch, and keep me in touch with friends and colleagues. Some of these apps also integrate Salesforce, Yammer, and BaseCamp for job-specific context and assistance.
Among the most popular apps, Google Now personalizes recommendations and assistance by applying predictive analytics to data stored in email, contacts, calendar, social, docs, and other types of online services users opt in. Other examples include Tipbit applying predictive analytics to make a more intelligent inbox, and EasilyDo using the notification system to recommend ways to automate common everyday tasks. Expect Labs is tackling this space from the other end of the spectrum, offering an intelligent assistance engine for enterprises to plug into and add proactive features to their own apps.
Here’s what we think:
• Vendors will experience burnouts and early customer frustration, much like in voice recognition. In the music industry, it’s said that an artist is only as good as her last hit. We saw that analogy apply to voice recognition when users got frustrated at Siri as soon as she failed once on them. Expect a similar dynamic with all types of predictive apps.
We attended the recent Glimpse Conference 2013, where members of New York's tech scene came together at Bloomberg headquarters to talk about social discovery, predictive analytics, and customer engagement.
Our key takeaway from the event: small, real-time data coming from very personal apps like email, calendar, social, and other online services will fuel next-level predictive apps and services. Specifically:
• Better insight doesn’t require more data; it needs the right data. Amassing large databases of customer profiles, purchase history, and web browser activity only goes so far, and is costing companies millions, if not billions of dollars every year. Mikael Berner from EasilyDo sees a new opportunity in better utilizing data scattered across personal email indices, calendars, social networks, and file and content repositories that directly indicate customers’ plans, interests, and motivations.
• Email, calendar, and location data is a goldmine for predictive analytics. Expedia or TripAdvisor can track web activities to recall a user searched for hotels last November and is likely to travel again this year, but a flight confirmation sitting in email or vacation time logged in calendar is a much stronger indicator of travel plans.
Forrester's global analysts have written some great pieces on gamification. In general terms, this research is is just as applicable to the SE Asian markets. However, there are some specific differences within the region that should also be considered. The most important thing to remember is that, while the general principles of gamification definitely hold true within the region, there are still some specific differences that should also be taken into account.
First and foremost, we definitely see the same problems in APAC where a lack of clarity on the desired behaviour encourages game play - for games sake. This is probably the worst outcome of all for gamification initiatives, regardless of where they're deployed. If there's no clear desired behaviour change identified, there's absolutely no valid reason to introduce gamification. The real challenge though is ensuring that the right strategy is selected to achieve the right objectives.