Tag Archives: Gary Angel

The Uses of In-store Customer Journey Data – Store Marketing

I’m working my way through the broad uses of in-store customer journey optimization. I started with Store Layout and Merchandising optimization – which is really the foundational analytic capability that this type of data provides. Today, I’ll tackle a use that’s nearly as fundamental – optimizing in-store promotions. For those of you from the digital world, you can think of these two applications as parallel to site optimization and digital marketing optimization.

Promotion Planning

In-store promotion planning is one of those constant grinds in the life of retail analysis. You never stop planning promotions and you never get good enough. With PoS data, it’s pretty easy to measure the single most important aspect of a promotion – how much it sold. It can be a lot harder, however, to answer questions about why something worked or, as is often more salient, why something didn’t. In-store measurement can fill in the gaps around performance measurement AND help develop new promotion and display strategies.

With in-store journey measurement, you can track how and whether a promotion shifted behavior. Did a promotion steer visitors to a section? Did it keep them there longer? Did it drive key milestones like staff interaction or dressing room decisions? With only PoS data, you can easily misunderstand what drove a promotion’s apparent effectiveness. Almost as important, in-store journey measurement provides unique insight into how a promotion cannibalized shopping behaviors and generated new opportunities. When you change navigation patterns in the store, you ALWAYS cannibalize some behaviors and you nearly always disadvantage some sections/products. You also create new opportunities and traffic corridors that might present additional optimization or promotion opportunities. Understanding how cannibalization and redirection worked and whether or not their impact outweighed the promotion benefits is essential to developing sound long term strategies.

And it’s not all about the customer. In digital analytics, we didn’t have to worry much about compliance issues. What you pushed to the website is what was on the website. With dozens, hundreds or thousands of stores to manage, though, pushing content and making sure it’s consistent and correctly deployed is no joke. In-store customer journey measurement provides a strong behavioral compliance check. When a promotion drives specific patterns of behavior, it’s easy to see which stores are roughly following the pattern and which aren’t – given you near real-time feedback on potential compliance issues.

 

Questions you can Answer

  • Why did a promotion work better or worse than expected?
  • How did promotions localize and were there stores that didn’t “play along”?
  • How much opportunity did promotions have to influence shopping?
  • How successful were shoppers who were exposed to the promotion?
  • Did the promotion create new “impulse” opportunities?
  • Did the promotion cannibalize other areas/products and to what extent?
  • For a potential promotion, what are they placement areas that will drive exposure to the right shopping segments?
  • Were there stores that didn’t deploy or correctly implement a promotion?

Next up? A really powerful and oft-neglected aspect of customer journey measurement – staff optimization.

What is in-store customer journey data for?

In my last post, I described what in-store customer data is. But the really important question is this – what do you do with it? Not surprisingly, in-store customer movement data serves quite a range of needs that I’ll categorize broadly as store layout optimization, promotion planning and optimization, staff optimization, digital experience integration, omni-channel experience optimization, and customer experience optimization. I’ll talk about each in more detail, but you can think about it this way. Half of the utility of in-store customer journey measurement is focused on you – your store, your promotions and your staff. When you can measure the in-store customer journey better, you can optimize your marketing and operations more effectively. It’s that simple. The other half of the equation is about the customer. Mapping customer segments, finding gaps in the experience, figuring out how omni-channel journeys work. This kind of data may have immediate tactical implications but it’s real function is strategic. When you understand the customer experience better you can design better stores, better marketing campaigns, and better omni-channel strategies.

I’m going to cover each area in a short post, starting with the most basic and straightforward (store layout) and moving up into the increasingly strategic uses.

 

Store Layout and Merchandising Optimization

While bricks&mortar hasn’t had the kind of measurement and continuous improvement systems that drive digital, it has had a long, arduous and fruitful journey to maturity. Store analysts and manager know a lot. And while in-store customer journey measurement can fill in some pretty important gaps, you can do a lot of good store optimization based on a combination of well-understood best practices, basic door-counting, and PoS information. At a high-level, retailers understand how product placement drives sales, what the value of an end-cap/feature is, and how shelf placement matters. With PoS data, they also understand which products tend to be purchased together. So what’s missing? Quite a bit, actually, and some of it is pretty valuable. With customer journey data you can do true funnel analysis – not just at the store level (PoS/Door Counting) but at a detailed level within the store. You’ll see the opportunity each store area had, what customer segments made up that opportunity, and how well the section of the store is engaging customers and converting on the opportunity. Funnel analysis forever changed the way people optimized websites. It can do the same for the store. When you make a change, you can see how patterns of movement, shopping and segmentation all shift. You can isolate specific segments of customer (first time, regular, committed shopper, browser) and see how their product associations and navigation patterns differ. If this sounds like continuous improvement through testing…well, that’s exactly what it is.

Questions you can Answer

  • How well is each area and section of the store performing?
  • How do different customer segments use the store differently?
  • How effective are displays in engaging customers?
  • How did store layout changes impact opportunity and engagement?
  • Are there underutilized areas of the store?
  • Are store experiences capturing engagement and changing shopping patterns?
  • Are there unusual local patterns of engagement at a particular store?

Next up? Optimizing promotions and in-store marketing campaigns.

 

Optimizing Omni-Channel with Analytics from the In-Store Customer Journey

I’m going to be co-hosting a webinar with my friend John Morrell at Datameer on Omni-Channel Analytics and using In-Store Customer Journey Data. It should be pretty cool stuff – and, of course, it’s free!

You can register here!

What is In-Store Customer Journey Data?

Analytics professionals love data and technology. So it’s easy for us (and I use “us” because I completely self-identify in both the category of analytics professional and someone who loves data and technology) to get excited about new data sources and new measurement systems – sometimes without thinking too carefully about what they are for or whether they are really useful. When I first got interested in the technologies to track in-store customer journeys, I’ll admit that its newness was a big part of its appeal. But while newness can get you through a “first date”, it can’t – by its very nature – sustain a relationship. In the last few months, as I’ve worked on designing and building our initial product, I’ve had to put a lot of thought into how in-store measurement technology can be used, what will drive real value, and what’s just “for show”. In my last post, I described using the “PoS Test” (asking whether, for any given business question, in-store customer journey data worked better or differently than PoS data) to help choose the reports and analysis that fit this new technology. But I can see that in that post I put the cart somewhat before the horse, since I didn’t really describe in-store customer journey data and it’s likely applications. I’m going to rectify that now.

To measure the in-store customer journey you track customers as they move through your physical environment. The underlying data is really a set of way-points. Each point defines a moment in time when the customer was at a specific location. This is the core journey measurement data.

By aggregating those points and then mapping them to the actual store layout, you have data about how many people entered your store, where they went, and how long they spent near or around any store section. This mapping to the store is the point where concerns about accuracy crop up. After all, the waypoints themselves don’t have any meaning. It’s only when they are overlaid on top of the store that they become interesting. The more precisely you an place the customer with respect to the store, the more you can do with the data.

By tracking key waypoints along the journey (such as dressing rooms or registers), the basic journey data can be used to help build an in-store conversion funnel. Add Point-of-Sale data (and you’d be crazy not to) and you have the full conversion funnel at a product level and all the experience that went with it. For those coming from a digital world, this may feel like the complete journey. It has everything we measure in the digital world and can support all of the same analytic techniques – from funnel analysis to functional and real-estate optimization to behavioral segmentation. But in physical retail, there’s an additional, critical component: measuring staff interactions. It’s hard to overstate the importance of human interactions in physical retail; so if you want to really map the in-store customer journey you have to add in associate interactions. For any given customer journey, you’ll want to know whether, when, how long and with whom a customer interacted.

For most stores, this combination of journey waypoint data, store mapping, PoS data, and staff interaction data is the whole of customer journey data and it’s powerful. At Digital Mortar, though, we’re trying to build a comprehensive measurement backbone for the store that includes detailed digital experiences in store (mobile, digital signage, and specialized in-store experiences) AND a set of variables that encompass the background environment for a customer visit.

In-store digital experiences are a key part of a modern retail customer journey and if you can’t integrate them into your omni-channel picture of a customer you don’t have key ingredients of the experience. I also happen to believe that custom digital experiences will be a crucial differentiator in the evolution of retail experience.

What about the background environment – what does that mean? There’s a lot more environment in physical retail than there is in digital. Weather, for example, is a critical part of the background environment – impacting store traffic but also dramatically changing in-store journeys and purchase patterns. Other important environment variables include store promotions (local and national), advertising campaigns, mall traffic and promotions, road traffic, events, what digital signage was showing and even what music was playing during a customer visit. The more environment data you have, the better chance you have of understanding individual customer journeys and figuring out what shapes them in meaningful ways.

 

Summing Up

The in-store customer journey data begins with the waypoint data. That’s the core data that describes the actual customer experience in the store. To be useful, that data has to be mapped accurately to the store layout and the merchandise. You have to know what’s THERE! Integrating PoS data provides the key success metrics you need to understand what parts of the experience worked and to build full in-store funnels. Associate interactions data adds the human part of the experience and opens the door to meaningful staffing optimization. And the picture is completed by adding in digital interaction data and as much background data as you can get – particularly key facts about weather and promotions. Taken together, this data provides remarkable insight into the in-store funnel and customer experience. And to prove it, my next post will tackle the actual uses of this data and the business questions it can (and should) answer!

Why do we need to track customers when we know what they buy?

Digital Mortar is committed to bringing a whole new generation of measurement and analytics to the in-store customer journey. What I mean by that “new generation” is that our approach embodies more complete and far more accurate data collection. I mean that it provides far more interesting and directive reports. And I mean that our analytics will make a store (or other physical space) work better. But how does that happen and why do we need to track customers inside the store when we know what they buy? After all, it’s not as if traditional stores are unmeasured. Stores have, at minimum, PoS data and store merchandising and operations data. In other words, we know what we had to sell, we know how many people we used to sell it, and we know how much (and what and what profit) we actually sold.

That stuff is vital and deeply explanatory.

It constitutes the data necessary to optimize assortment, manage (to some extent) staffing needs, allocate staff to areas, and understand which categories are pulling their weight. It can even, with market basket analysis, help us understand which products are associated in customer’s shopping behaviors and can form the basis for layout optimization.

We come from a digital analytics background – analyzing customer experience on eCommerce sites we often had a similar situation. The back-office systems told us which products were purchased, which were bought together, which categories were most successful. You didn’t need a digital analytics solution to tell you any of that. So if you bought, implemented and tried to use a digital analytics solution and those were your questions…well, you were going to be disappointed. Not because a digital analytics solution couldn’t provide answers, it just couldn’t provide better answer than you already had.

It’s the same with in-store tracking systems; which is why when we’re building our system, evaluating reports or doing analysis for clients at Digital Mortar, I find myself using the PoS test. The PoS test is just this pretty simple question: does using the customer in-store journey to answer the question provide better, more useful information than simply knowing what customers bought?

When the answer yes, we build it. But sometimes the answer is no – and we just leave well enough alone.

Let me give you some examples from real-life to show why the PoS test can help clarify what In-Store tracking is for. Here’s three different reports based on understanding the in-store customer journey:

#1: There are regular in-store events hosted by each location. With in-store tracking, we can measure the browsing impact of these events and see if they encourage people to shop products.

#2: There are sometimes significant category performance differences between locations. With in-store tracking, we can measure whether the performance differences are driven by layout, by traffic type, by weather or by area shop per preferences.

#3: Matching staffing levels to store traffic can be tricky. Are there times when a store is understaffed leaving sales, literally, on the table? With in-store tracking we can measure associate / customer rations, interactions and performance and we can identify whether and how often lowered interaction rates lost sales.

I think all three of these reports are potentially interesting – they’re perfectly reasonable to ask for and to produce.

With #1, however, I have to wonder how much value in-store tracking will add beyond PoS data. I can just as easily correlate PoS data to event times to see if events drive additional sales. What I don’t know is whether event attendees browse but don’t buy. If I do this analysis with in-store tracking data, the first question I’ll get is “But did they buy anything?” If, on the other hand, I do the analysis with PoS data, I’m much less likely to hear “But did they browse the store?” So while in-store tracking adds a little bit of information to the problem, it’s probably not the best or the easiest way to understand the impact of store events. We chose not to include this type of report in our base report set, even though we do let people integrate and view this type of data.

Question #2 is quite different. The question starts with sales data. We see differences in category sales by store. So more PoS data isn’t going to help. When you want to know why sales are different (by day, by store, by region, etc.), then you’ll need other types of data. Obviously, you’ll need square footage to understand efficiency, but the type of store layout data you can bring to bear is probably even more critical than measures of efficiency. With in-store tracking you can see how often a category functions as a draw (where customers go first), how it gets traffic from associated areas, how much opportunity it had, and how well it actually performed. Along with weather and associate interaction data, you have almost every factor you’re likely to need to really understand the drivers of performance. We made sure this kind of analytics is easy in our tool. Not just by integrating PoS data, but by making sure that it’s possible to understand and compare how store layouts shape category browsing and buying.

Question #3 is somewhere in between. By matching staffing data to PoS data, I can see if there are times when I look understaffed.  But I’m missing significant pieces of information if I try to optimize staff using only PoS data. Door-counting data can take this one step further and help me understand when interaction opportunities were highest (and most underserved). With full in-store journey tracking, I can refine my answers to individual categories / departments and make sure I’m evaluating real opportunities not, for example, mall pass throughs. So in-store journey tracking deepens and sharpens the answer to Staffing Gaps well beyond what can be achieved with only PoS data or even PoS and door-counting data. Once again, we chose to include staff optimization reports (actually a whole bunch of them) in the base product. Even though you can do interesting analysis with just PoS data, there’s too much missing to make decision-makers informed and confident enough to make changes. And making changes is what it’s all about.

 

We all know the old saying about everything looking like a nail when your only tool is a hammer. But the truth is that we often fixate on a particular tool even when many others are near to hand. You can answer all sorts of questions with in-store journey tracking data, but some of those questions can be answered as well or better using your existing PoS or door-counting data. This sort of analytics duplication isn’t unique to in-store tracking. It’s ubiquitous in data analytics in general. Before you start buying systems, using reports or delving into a tool, it’s almost always worth asking if it’s the right/easiest/best data for the job. It just so happens that with in-store tracking data, asking how and whether it extends PoS data is almost always a good place to start.

In creating the DM tool, we’ve tried to do a lot of that work for you. And by applying the PoS test, we think we’ve created a report set that helps guide you to the best uses of in-store tracking data. The uses that take full advantage of what makes this data unique and that don’t waste your time with stuff you already (should) know.

 

Digital Transformation Dialogues – Part 4 – Creating the Right Culture around Collaboration Tools

[Here’s more from my ongoing dialogue with transformation expert and friend Scott K Wilder. In the last post, we discussed ways to make an older workforce more digitally savvy. Scott ended that post with this: “Personally, I would rather be HipChatted vs. Slacked. But technology sometimes like religion. You have to find out what people are most comfortable with. At Marketo, it was Slack. At Salesforce, it is Chatter. For me, I prefer to be Skyped!. How about you?”]

GA: I’m a reluctant video user. I was always the kid who liked to sit in the very back of the class hunched down behind somebody who played Right Guard on the football team. That being said, I have some issues with chat too. It’s a very interruptive technology. I know that’s it’s super popular with developers – and I see the point particularly in Agile teams. But I always viewed serious code writing as essentially monastic. That may seem ludicrous, but writing large scale software is a real intellectual undertaking – requiring you to hold hundreds of thousands of lines of code in your head and have at least a general sense of how they fit together and what’s there. I’m not convinced you can do that while you’re regularly dropping in and out of chat sessions (or, for that matter, having meetings every 30 minutes). When I was writing large-scale code I pretty much talked to no one. Of course, a vanishingly tiny percentage of people are writing serious code. But I feel the same way about writing – something I do regularly. When I’m writing a piece I care about, I seriously don’t want to be interrupted. So my question really is about protecting culture – you’ve talked about adoption – and creating a culture of usage. I agree that’s important – in fact it’s a far more common failure point. Life being what it is, though, we also have to worry about too much success (and part of adoption is assuring people that culture won’t change too much – even if it will). So how do you create an etiquette culture around collaborative technologies that protects other types of behavior we value? After all, no company wants the family equivalent of everyone whipping out their iPhones at the dinner table…

SW: Ah, now we are getting into a little psychology and ethnography. For me, there are two ways to approach this (business) issue:

  1. Constantly try to understand the different personalities in your company
  2. Consistently establish and communicate company values throughout the organization

In every organization, there are many personality types. Each responds to new challenges in different ways, especially when it comes to adopting new technologies. Individual or team behaviors can be looked at through a Myers Briggs lens. Or you can examine various personas involved.

Ironically, 80% of companies do market segmentation with personas or some other kind approach, but few take the time to do the same thing when trying to figure out how to work with their own employees. Few companies step back and look at the different ways their own people adopt technology. There is often little conversation about how new processes and technologies diffuse throughout an organization. So what’s my point about all of this psycho mumbo jumbo.

Before you can create a culture around the adoption of a new technology, you need to understand the different personality types in your organization. And it helps if you leverage a topography like Myers Briggs, to help understand how people learn or adopt new technologies.

For example some people might prefer to learn on their own either studying a user manual or watching videos before kicking the tires and testing out a new product. Others might prefer to learn with a mentor or teacher to guide them. And others might want to learn by participating with others. The important thing is to first understand how an individual responds and adapt to new ways of doing business.

After you know the different types of people/personalities you are dealing with, you can begin to focus a culture that fosters the adoption of new technologies while protecting people’s values (or how they want to start using the new technology).

Finally, the challenge is getting these different types of people to function on a day-to-day basis with each other. This will be easier if you have provide a comfortable and safe environment for them to learn at their own speed and in their own way.

Secondly, when creating an etiquette culture around collaborative technologies, it’s important to present them to your employees by showing how they map to your core company values (This assumes you have company values). Atlassian, my current employer, has very strong values which are reviewed every time the company works on a project. Some of them include:

  • Don’t #@!% the customer: This statement promotes honesty and transparency. The company knows that their Customers are their lifeblood. Without happy customers, they are doomed
  • Play, as a team: As they say “We spend a huge amount of our time at work. So the more that time doesn’t feel like “work,” the better. We can be serious, without taking ourselves too seriously. We strive to put what’s right for the team first – whether in a meeting room or on a football pitch.”

These are just two of the values. There are others, but each one is used to help keep every employee aligned and heading towards the company’s True North, especially when adopting a new collaborative technology or trying to change behavior across the organization.

Finally, collaboration has no beginning nor end It is a continuous journey that involves multiple parts of your organization.

GA: There’s a lot here to respond to. I’m totally on board with your thoughts around corporate culture and values. Most companies pretend to have values – some actually do. And while I’ve argued in some other cases that you can drive analytics without necessarily having top-down support (though it sure does help), culture building is either hierarchic or anarchic – and anarchic rarely works as a model. That isn’t to say that individual managers can’t create micro-cultures inside a larger organization. They do – and pretty constantly. But those micro-cultures – for good or ill – are always getting worn down and eroded by the broader culture. There’s no place where the impact of senior folks is more pronounced than on setting the tone for this kind of culture building – and, as I’ve argued elsewhere, culture building isn’t done with words. In the beginning was the deed! You can talk “Don’t #@!% the customertill you’re blue in the face, but the first time an executive makes a decision to the contrary, all that talk will be less than worthless (and I do mean less since it creates negative value in the company). That’s one good reason why it’s important to have values you A) actually care about and B) can reasonably live up to.

I’m less comfortable with tests like Myers-Briggs for employee segmentation. I’ve never been confident that personality tests capture anything real. I know they have a lot of fans (and a lot of fans among people whose opinions I respect) – but I’m unconvinced. Sure, we all see ourselves in the results of these tests. But we see ourselves in our horoscopes too. Self identification isn’t objective verification. But I’ll give you the validity of personality types and still question whether it’s a good tool to help drive cultural adoption (and proper etiquette) around social technologies. I’ll buy that segmentation would bring something to crafting a change management and adoption strategy – but would I use personality types or would I use things like rank, role, and behavior?

Convince me if you can!

Finally, let’s talk technology. I’d love to get your thoughts on what types of collaborative technologies make the biggest difference in an organization. And I’d also like your thoughts on whether that’s even the right question. Do you need to think about a collaborative suite? Will one tool likely die on the vine where a constellation of tools might work? I’ve seen both approaches fail – but that’s never conclusive. We live in a “baseball” world where failure is always the most common outcome.

 

Digital Transformation Dialogues – Part 3 – Bringing an Older Workforce up to Speed and Driving Adoption of Digital Tools

[Here’s more from my ongoing dialogue with transformation expert and friend Scott K Wilder. In the last post, we discussed the role of Millennials in balancing an older workforce. But I wanted a little more detail on how to get an older workforce more digitally aware…]

SW: I probably forgot this one because I am an older guy, but I’m also someone who thinks it’s every marketer’s responsibility to learn digital technology. Before I directly answer the question, let me give you an example. My son is really into drones and wants me to take him to some national parks so he can fly his drone. Before I make a road trip with him, however, I want to master drones, so I hired a drone coach. After all, I am the one who is ultimately responsible for my son’s safety. Working at as a Digital Marketer or Digital Employee requires the same commitment. The only difference, however, is that companies need to play a bit of the parental role and provide a clear path for their employees to learn about technology.

This can be done by paying for courses (Marketo, my former employer, pays for its employees to take courses at Lynda.com). It can be done by making ‘learning certain technologies’ as required for the job. Instead of saying you learn it or you lose it (your job), position this change as an opportunity to skill up — and that the company is investing in the future (in its best asset, its employees).

Companies also should provide career guidance — either for older employees to find other opportunities within their company or with a company’s partner. Training, career guidance are not only great retention tools, but also build loyalty after an employee moves on.

Companies also need to gently require that digital technologies be used in their everyday business practices. If the older person wants to remain part of the company, they will have to hop on the digital bus. And like the Magic School Bus (a book my kid loves), it will be a journey into unknown — with lots of opportunity to learn, a bit of uncertainty and a fun adventure. You know what. Even outside the office, they will feel as if they are on the Magic School Bus because by learning technology, older folks can have a more enriched life. My son Facetimes and Skypes with his Grandma twice a week.

Why should companies do this? Why should they make this investment? Several reasons, such as older workers tend to be loyal, older workers already know ‘your business’. Companies should also build incentive systems — gamify their career development — so they will be motivated to take on the exciting challenge of improving their skills.

Final note: Being Digital is more than just using the internet and Facebook. Companies should also figure out what digital technologies will help these older workers do their job better. If they need to be on social media, teach them Hootsuite. If they need to manage email programs, teach them Autopilot or Exact Target. If they need to collaborate better, be their guide while they learn Slack or HipChat.

GA: There’s a couple of points that I want to particularly call-out there. One is that company’s aren’t taking full advantage of the explosion in high-quality educational courseware that’s available these days. Sure, lots of folks will do this on their own, but not everyone is sufficiently motivated. I’ve always said my number one guiding principal – and the reason transformation is so hard – is that EVERYONE IS FUNDAMENTALLY LAZY. Giving people real incentives and formal guidance on courseware so that it’s part of an employee’s basic career development is really easy to do and I think pays tremendous dividends. If your company hasn’t curated public courseware for specific career-tracks and incentivized your employees to take advantage, you should be kicking your HR team’s butt (just my humble opinion).

I’m also a huge fan of the idea (as you know) that people have to DO stuff. And I’m glad you brought up the technologies because that’s the next (and last) area I wanted to explore. A lot of the digital technologies are fundamentally collaborative. But that can make adoption critical to their success. I know you’ve been living this problem – how do you get a team (and keep my older, non-digital workers in mind) to adopt tools like Slack?

SW: Gary, why are you always asking me the hard questions? I think you ‘re correct in focusing on ‘the team’ vs. ‘the company’ and trying to mandate day 1 that a whole company start using something like Slack.

They key is to start with one group.  Pick a team that seems receptive to taking on new ways of doing things — especially when it comes to digital technology. And within that group, you should also identify a few key digital change agents, early adopters, who are willing to not only try out the new technology, but also be champions for it.

Create a program for these digital champions. It can be rewards focused, but even better,  show them how sharing their knowledge and experience will help them learn a new technology even better and make them more marketable. Intuit, where I spent almost a full decade, has a philosophy called “Learn Teach Learn.” The only way to really learn something is to teach it to others (Intuit has a great learning culture!).

Of course, there is another option. You could see if any group in the company is currently using Slack and make them that group ‘your change agents. At Marketo, it was actually the company’s commuters — employees who took a small shuttle bus that looked like one of those vans old age homes use to transport its frail residents – who started using Slack. They let their fellow workers know if they wanted the bus to wait for them or if they wanted the van to turn around and pick up someone they forgot. My group of commuters called our Slack group, The Purple Lobster.

The Slack group was called the Purple Lobster because that’s what we called the van. We picked purple because that was Marketo’s company color. And lobster because it wasn’t the fastest moving vehicle on Highway 101.

And like a lobster slithering in the sand (sorry about pushing the poetic envelop here) slowly, but surely ,other commuting groups started to using Slack. Eventually, product teams started using it And finally, the CTO and his team made the call to not fight the crowd and force the company to use another tool, like Chatter. Instead, CIO convinced his fellow executives to adopt Slack across the company. It was a brilliant ‘if you can’t beat them, then join them’ strategy.

If you identify a group using Slack, challenge them to go completely cold Turkey. See if they are willing to only use Slack only (no email) for a week or so. At Atlassian, I had my hand Slacked when I tried to send an email to someone with a simple question. They recommended I use their Slack like product, Hipchat. And now, I only have 20 emails in my inbox. How many of you have only 20 emails in your corporate email inbox?

If you are not so lucky to find early adopters, you need to find a group of people who are most like to use the new technology. If there are some older folks on the team, pair them up with the younger wipper snappers. Or provide some training.

The key in all this is not to focus on technology. Instead, treat the change to Slack or any other digital technology as a change management exercise. Focus on adoption — education, onboarding and engagement. None of this should be done in a vacuum. You need someone to shepherd the process. Someone who can be a guide, a teacher, a problem solver and yes, a true change agent.

Other considerations include rewarding people for their efforts and successes. Gamify the process! In doing so, make sure to acknowledge people’s efforts for trying. Don’t make the same mistake most schools make and only pass people for knowing the answer. As Carol Dweck, well known motivational researcher,  points out, children praised for hard work chose problems that promised increased learning (vs. just getting the right answer). This also applies to adults. Really!

The key here is to alter someone’s mindset. Instead of rewarding (just giving them a bonus) or punishing someone (not promoting them) for adopting a new technology, recognize their effort and hard work. The end result will be they might adopt taking on new challenges and succeeding at them. Even if it means learning and using something like Slack.

Finally good old training is important. It always amazes me how many companies introduce a new technology and offer one time training. Usually during a three hour class. If you are licensing a technology like Slack, see if they can conduct monthly webinars to answer questions (if not, you offer it). Also have videos and Q&As available for your staff.

Personally, I would rather be HipChatted vs. Slacked. But technology sometimes like religion. You have to find out what people are most comfortable with. At Marketo, it was Slack. At Salesforce, it is Chatter. For me, I prefer to be Skyped!. How about you?