aheadCRM - Blog #Future

Customer Service in a World of Ambient Computing - The Service Center View

Fri, 01 Sep 2017 19:52:15 -0400

A few weeks ago I wrote an article about customer service in a world of ambient computing. This article looked at customer service from a customer’s point of view. In it I described how I see customer service getting humanized again by leveraging the advances in AI technologies like Natural Language Processing, speech-to-text- and text-to-speech generation along with intent determination. Leveraging these technologies customer service will turn into a conversation and it won’t matter anymore whether service is delivered by a bot or by a human. For the customer it will all appear to be the same. Instead of FAQs or web searches, bots will be the first line of support and escalate a problem to humans if they cannot solve it on their own. The obvious question is whether there will be an impact on the customer service center? And it probably does. Call centers, and with it the service agents as well as their managers, already now are under intense pressure to deliver, and to deliver more efficiently. With the increasing use of call deflection technologies like FAQs and communities there is a trend for the incidents facing the agents becoming more challenging. For example Helpshift states that already with its technology it is able to deflect about 90% of all incidents, which are solved via the native in-app FAQ that is delivered by the them. This statement basically says that the support staff is basically relieved of dealing with simple matters but has the chance to take up the more challenging ones. Still, in a world of ambient computing any given app can have hundreds of millions of users. Let’s say that any given day just one per cent of 100 million users have an issue. The well working FAQ deflects 99% of these. That leaves the service center with 100,000 calls. In one day. And they are the harder ones. Still, let’s be optimistic and say that an agent can solve 10 issues an hour, giving him 80 in an 8 hour shift. This would mean an overall call center size of 1,250 agents is needed to cope with this demand. Each of them under a tremendous stress level. With the systems behind bots becoming more and more intelligent the difficulty of raised issues will increase, even if FAQs and web searches are essentially hidden behind a bot interface that essentially makes the human agent the second point of contact again as opposed to the third, which likely means that the customer’s level of annoyance is slightly less elevated than in a third level scenario. At the same time it seems that call center agents are not prepared for handling this stress level. The employee turnover rate remains high and is probably even rising.

What Does This Mean For The Service Center?

Call centers are therefore facing a double challenge

Contain cost. This is achieved by more automation, which in turn puts more strain on the employees
Employ and retain a highly skilled set of service agents, which additionally have matching character traits, which drives cost. Skilled people tend to be more expensive than unskilled ones, and moving a call center into a low-salary country helps only so much – if at all. Training comes at an expense as well. This will be somewhat augmented by reduced hiring cost

The solution to it will be multi-faceted and increase a trend that is already visible. Implement intelligent systems that more than offset the higher salaries demanded – and deserved – by the fewer call center agents, through an increased solution rate and through them being of more help to the service agents. These systems will significantly rely on machine learning out of a variety of sources Employ communities by incentivizing to other users to help other users. These communities will be managed by community managers, with increasing support by AI-driven bots. Highly data driven prioritization and intelligent grouping and routing of incidents to the best matching agent, bot or human. This will involve sophisticated Natural Language Processing capabilities but will help in solving multiple calls regarding the same problem in one process Improved collaboration, bot – bot, bot – human, human – bot, human – human, to further increase the service center’s efficiency. Bot to bot collaboration and bot to human collaboration are for smooth handovers, as for the foreseeable future bots will stay focused on narrowly defined scopes. Human to human collaboration is again a smooth handover to the right expert, but is also about educating the colleague by helping out with own specialized experience. Finally, human to bot collaboration is about the human training the system on the go. Last, but not least, by hiring the right people. An early 2017 study by Harvard Business Review on Kick-Ass Customer Service revealed that call center managers are hiring the wrong people. In scenarios that increasingly deflect calls it needs more highly trained controllers and rocks with a mindset for collaboration, rather than empathizers. While empathy is important what matters most when dealing with a customer in an aggravated mood is a fast and efficient resolution. With this, the role of the manager will change, too, into the direction of being a servant to the team and taking care of roadblocks for the agents and fostering collaboration. This collaboration mandatorily extends into the product department. The best issue is the one that doesn’t even occur. Data from the call center, and from the app itself, gives unique insight into possible problem patterns. And the best problem to have is the one that doesn’t even occur. DevOps gives an idea on how this can get achieved. Ah yes, don’t script too much. Scripts are a good guidance for someone unknowledgeable, which the future call center agent is not. The future of the call center lies in a high degree of automation, powered by highly skilled and motivated agents. That brings the human back to customers and agents alike.

Ambient Computing and the Future of Mobile Apps

Thu, 24 Aug 2017 07:34:17 -0400

A short while ago Craig Rentzke from Helpshift pointed me to a particular episode of CXOTalk, featuring Kevin Henrikson of Microsoft and professor Anindya Ghose from NYU. Henrikson is responsible for Microsoft’s Outlook for Mobile, a personal information manager (PIM) app, whereas prof. Ghose comes more from a B2C angle, with B2C being more concerned with convenience. This interesting episode deals with the future of mobile computing and given that, apparently about how mobile apps will (have to) look like and what it is that vendors should do and what they should not do with the apps.

The Now

Naturally, the discussion immediately zeroed in on two topics

the purpose of the app
and data

The purpose of the app mainly determines two things, which are first the way that users are presented with information and are engaging with the app and second the data that gets collected and used in order to (positively) influence the user experience while considering their privacy. The data that gets collected needs to be used to provide the users with timely and relevant information, which does not only benefit the vendor but also, and chiefly, the user. That the data collection ‘behaviors’ of especially B2C apps are not hitting that objective is probably the industry’s worst kept secret. The apps collect more than necessary and use it for a very wide range of purposes, mostly wider than the users are aware of. They basically strip the user of their personal data. This realization was also what led a friend of mine and me found Epikonic, with the clear intention of giving users a choice who they interact with instead of ‘being chosen’ by companies (sorry for this shameless plug, actually, well, not so). On the productivity app side the picture is far better. Users need to be able to do their job efficiently and easily. In the case of a PIM app this means that users are checking it frequently and need to be able to find and do what they need with minimal time spent, including getting support. Being a user of Outlook for Mobile I can say that the team of Microsoft and Helpshift are succeeding here. The app collects telemetry data and can suggest appropriate attachments and certainly provides very relevant information to the service center in case of a call for support. The proof is more than 100 million installations that are getting served only on Android. However!

The Then

I do think that both discussion partners did not look far enough into the future of what mobile apps will be. They remained on grounds within the current paradigm, which is a mobile app that serves a specific purpose and that resides on a mobile device- aka smartphone, competing for real estate on the phone’s main screen. Yes, that paradigm covers more use cases than currently deployed, including food and drink ordering from the airline app to the flight attendants without reaching out for the button on the ceiling or using the built-in screens – if they are there at all. And this describes only one possible user initiated use case in one particular industry. Still, this paradigm is challenged already now.

It is hard to make users use an app for a longer time, think of (re-) engagement campaigns
Mobile devices are powerful enough to not require specific - very limited - app functionalities that serve one purpose. Think of apps become multi-purpose again
The single 2D screen itself becomes less important, think of smart watches, wearable sensors, AR, VR, and holograms
Scalability becoming a main theme. Think automation and bots
Interaction metaphors, other than those limited to a 2d screen are emerging – think of voice, conversations, eye tracking
Apps are becoming intelligent. Think of artificial intelligence, in particular deep learning
And then there is the Internet of Things.

There is a short-term future and a long-term vision. Helpshift is perfectly positioned for the former. Knowing CEO Abinash Tripathy, he also has a good idea for the long-term play.

The Next Level

Which goes beyond the smartphone. The app will be mobile – more so than now. The smartphone will no more be THE frontend that it is now. It will stay the switchboard of our lives that it has become, but it will move into the background, like the PC and laptop did. It will become more of a personal server. With lots of little personal devices that serve specific purposes, and other personal servers, situationally connected to it to support the user’s wishes in the here and now. This will lead to more intelligence at all levels, the personal device, the personal server, and the (interconnected) back ends. And all these little devices will have different UI’s and interaction metaphors, like VR, AR using eytracking, holograms that can be manipulated, etc. But most of all with speech becoming more and more important. I am looking forward to Helpshift participating in shaping this future. Be prepared.

Omnichannel – Myth, Reality or Utopia?

Tue, 01 Nov 2016 12:14:09 -0400

Omnichannel – is it a Myth, Reality or Utopia?

Over the past 20 or so years the way products and services get sold and customer service as well as marketing get delivered changed dramatically. Gone are the times where a potential customer was addressed via a radio- or TV-spot or an ad printed in a newspaper, advertising mail in the mailbox … – well, it still happens, but the focus shifted dramatically. We started off from one single ‘channel’ – customer goes to the store and interacts with a person – and added an ever increasing number of additional ones, like the ones mentioned, plus many more. For retail businesses the store will not go away. Generally spoken, human interaction will stay important, probably increase in importance; human customer service will not vanish – but is likely to change … please hold this thought. In today’s omnichannel world we also have telephone, e-mail, web-delivered ads, mobile apps, branded and white-label communities, social media like Facebook, Instagram, Twitter, etc., knowledge bases in combination with self-service, chat, messenger applications like WhatsApp, FB Messenger, Snapchat, iMessage; chat supported by ‘machine intelligence’, exposed via so called chatbots, and what not. The list could virtually go on and on. This is all supported by and implemented on a platform that leverages integrated applications, which work on a joint, or at least consistent data model – with clean data – utilizing strong real-time analytics capabilities that powers both, customer segmentation and knowledge categorization for efficient search. And it delivers a great customer experience.

In Real Life

Uhhm, I am just awaking from my dream … Omnichannel is currently all the rage. Be available where your customer is, be consistent regardless of the communications channel that the customer uses at any time, and regardless of the changes of channels the customer chooses to make. Use the knowledge and information of previous interactions to the customers’ benefit. I, too, think that it is the right idea, and approach – at least for now.

The History – and a Glimpse into the Future?

We started off with just one single channel. Every interaction happened in the store – and via word of mouth. That is easy enough to handle but then changed very soon, as described above. The situation changed to being in the need to support a low number of independent channels, especially in marketing, but also in service. Content, and knowledge, were created and disseminated into these independent channels. This got somehow optimized. The information was made available for use in several channels. Born was the notion of cross channel. From there we went on to think of and to support multi-channel concepts by integrating the various applications and their data stores. We arrived at the era of suites. However, additional channels emerged and thanks to cloud computing and decentral budgets as well as lacking trust into IT departments, the pendulum swings from suites to best-of-breed solutions – all the way back to the 90s. As a consequence, platforms start to emerge. The number of communications channels that need to be supported, grew, and continues to grow. Additionally, customers increasingly demand being addressed personally and relevantly, on the communications channel of their choice. And they want to be able to get to the information themselves, be it marketing-, product information or service documentation. This is largely thanks to advances in computing technology and the emergence of smart phones, and needs to be addressed by companies. So, we arrived at the era of omnichannel, where companies feel the need to achieve a seamless marketing, sales, and service experience, regardless of the communications channel a customer uses at any given time. Including as per yet unknown channels. Again, this is a lofty goal – very lofty! Meanwhile companies start to realize that they cannot achieve this goal in its full epic breadth and depth, especially as they are also confronted with the need to be profitable, which obviously prevents them from throwing an infinite number of dollars at solving this problem. The overwhelming complexity of this scenario simply cannot be fully accommodated for at this time. Job-to-be-done thinking and customer journey maps evolved, which gave the ability to provide touch points for customers that can get offered by the companies in a manageable way. Advanced segmentation helps in limiting the offer to different customer groups, thus increasing the company’s relevance. Combined with sufficient computing power the relevance of the delivered content can be increased, too. On the service side we see communities and self-service approaches being developed and employed. Still, there are many databases with different aspects of data/knowledge that are not integrated, due to above best-of-breed approach and lacking pre-existing platforms in many businesses.

The Crystal Ball

And all this doesn’t even take into account the physical world. Talk about retail stores and people! This fragmentation harms the customer experience. Again, neither the retail store nor human customer service will vanish! But in reality, there is no real omnichannel experience delivered, with the possible exception of a few technologically very advanced brands. Nor needs to be! Companies need to prioritize. Looking at current technical trends only few channels are really important. For one the human touch. Mobile; already now a third of all web access happens via mobile devices. Conversational interfaces are increasingly becoming important, too. According to Esteban Kolsky, customer service will look totally different from what it is now in ten years’ time. He is a firm believer in automation. So am I. https://twitter.com/ekolsky/status/791046753382338560 We are facing self-service and communities as the main channels for customer service and even pre-purchase decision making. There are estimations that 90 per cent or more of customer service interactions can be automated. And I really wouldn’t disagree. What is the first thing we do when confronted with an issue? We use Google to research a resolution. Information that we receive online from companies pre-purchase (and in service scenarios, too) will increasingly be delivered by explorative algorithms (AI’s). So, where are we bound to?

Businesses will develop a strong focus on mobile delivery of digital content, be it for marketing or service; this very well also means via voice (bots), and via humans. On the longer run automated voice and interaction via other devices than the smartphone will gain increased importance. Alexa, Google Assistant, Siri, and VR/AR are giving us some direction here.
We will see customers triage between branded apps that they will use for preferred businesses, ‘aggregator’ apps for needs based access (e.g., ‘I want to order food, but do not know what’, …), and finally messaging apps with their embedded company channels/bots. The latter two categories might merge. Neither category, app or messaging, will vanish; however, there will be a consolidation, as customers will not want to deal with dozens of apps on their mobile devices. This was one of the ideas behind Epikonic – maybe we just have been too early …
With the rise of messaging apps and newer protocols, especially WebRTC, the importance of telephony as a separate network/technology will fade away. Telephony, especially with businesses, will merge into the data/app stream, mainly helping both parties; with an advantage for the business, though.
Seamless interoperation of self-service or community service with human service, due to improved automation and ‘intelligent’ software; human intervention will become the exception, and if it comes to human intervention, there will increasingly be more relevant information available to be used by the agent.

This will not happen in the next few days, but it will. Watch the trends.

Bots can kill Customer Experience

Mon, 23 May 2016 11:27:28 -0400

Bots are all the rage currently. By the looks of it they are at the peak of the hype cycle. We will see their deep fall into the trough of disillusionment soon. After all the well-known examples based on the Facebook messenger are somewhat underwhelming, to formulate it carefully. There is not much artificial intelligence visible - nor needed - to provide services like these. They also come with a poor user interface. And this combination of hyped examples, mixing up chatbots and AI, has the potential to kill customer experience. They certainly kill the user experience. Unless, this is, that these machines already reached a level of intelligence that they are magic to my simple mind... Which I doubt. To be sure, there are AIs around that amaze us: IBM's Watson, Apples Siri, Microsoft’s Cortana, Google's Now, ... even Microsoft’s infamous Tay which got a pretty bad reputation in no time, to name but a few. Recently a whole class of graduate students didn't realize that their teaching assistant Jill Watson, an AI based upon IBMs Watson, was actually an AI and not a person. And I sincerely believe that in not so far future we will see AI in many places that is indistinguishable from a human. As Salesforce's Marc Benioff recently said we will have AI do things that we cannot even imagine right now. The potential is virtually endless (pun intended). But what we see right now being built standalone or embedded into messaging apps has nothing to do with AI and it often has a poor user interface. This needs to get fixed, or else the potential of falling back to an 90's customer experience becomes very real. I get the potential benefit of connecting to businesses via one single app. And I do think that it is the right way forward! After all this is the very principle that is behind my Epikonic platform with its app front end. But the way the Facebook Messenger looks right now benefits exactly Facebook, which doubtless has a fairly elaborate AI behind the scenes.

The Way Ahead

But enough of the rant. I do not want to be destructive but show a possible way ahead. Because I think that even the simple bots that we see right now have their value, while the end game is in interacting with full-fledged AIs that we perceive as humans, very patient humans. According to Wikipedia a chatbot or chatterbot is “a computer program which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatterbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern from a database.” This definition is generic enough that even a simple flower ordering app on the FB Messenger that leads a customer through a predetermined process can get sold as a bot. And this is part of the problem. In order to make bots more useful and therefore accepted we need to work on a few frontiers. And I am sure that this will happen as part of bots moving through the trough of disillusionment. Part of this process will also change our perception of what a bot is. We stop talking of bots (who is talking of social CRM anymore?) and/or will redefine where the value add for customers and businesses is, by answering two basic questions:

How does does a bot help my customers to better accomplish their “jobs to be done”? Be they ordering of flowers or organizing a family vacation next spring, or whatever else. This is the harder question to answer.
How does it help the companies using bots improving their business? This one is related to the first question but also touches efficiency, not only effectiveness.

Supporting this these are the main three things that I think research and industry need to work on are:

An improved definition of what a bot is, in contrast to a simple application that can achieve the same. There is no need for a “bot” that allows me to set reminders for myself or get travel tips or to subscribe to a newsletter. There is also no need for bots that fail at basic natural language detection. Scanning for keywords as it is still very common simply doesn’t cut the mustard. And the real value lies in a meaningful interaction that successfully imitates an interaction between humans. This definition also needs to involve a distinction between a user interface and the the underlying logic. I would argue that many so called bots are actually just another user interface that is plugged on top of existing applications
Standardization; so far it looks like the APIs for every platform are different. I do not think that all APIs will ever look the same but some basic services need to get standardized, also to make it easier for developers to deploy across different platforms.
The ‘killer bot’; at the moment we have bots that basically do the same as apps/applications. And then these ‘old style’ apps are often richer in functionality and more convenient to use. A useful bot needs to deliver a use that is hard to deliver for a conventional app. Some (rudimentary) candidates that I see are in the area of financial services, like Digit or Penny. But I can imagine many more uses in complex areas like healthcare or day-to-day self-organization. Key are rich, useful functionality in combination with a simple user interface and integration to other used applications. Text, admittedly is an interface that we are comfortable with, but that doesn’t make it a good one. How many people are struggling with typing?

Maybe it is worthwhile ceasing to speak of bots altogether when we just mean a user interface? Just thinking …

Value, Relevance, Convenience - The Future of Retail

Mon, 16 May 2016 18:23:52 -0400

the Future of Retail is, well, interesting. Retailers today face an increasingly fierce competition. This competition is both, between brick-and-mortar retailers as well as between online-retailers and brick-and-mortar. Amazon, for example, is eating an increasing share of department stores’ lunch. It already now is the second largest apparel retailer in the US. According to Morgan Stanley research, quoted in a recent business insider article:

“Internet retailers (led by Amazon) have added $27.8 billion to their apparel revenue since 2005, while dept stores have lost $29.6 billion,” … “This share loss appears at risk of accelerating given 1) Amazon’s bigger push into fashion, and 2) consumer willingness/acceptance to shop fashion through Amazon.”

Additionally, customers are increasingly demanding, which is fuelled by being better informed and by the willingness to leverage this information. As a result of both of these trends retailers are losing relevance. One of the main challenges facing retailers (and brands, btw) is that big scale online retailers can very strongly compete on price. They also have a strong edge in data, which fuels their online experience. But here is also the chief weakness of online retailers like Amazon: They are online retailers, which confines the experience that they can offer to, well, online. Consumers used and use stores for showrooming to get a physical experience of the product and/or service. This is a clear indication that online is not everything! Which is one of the reasons why Amazon experiments with Internet of Things devices like their Dash button, which they recently enhanced with an SDK; it does also explain why Amazon is experimenting with kiosks and retail stores. The chief differentiator of a brick-and-mortar retailer is that they have physical presences, retail stores. In these stores it is possible to interact with product and with people. This is something that an online retailer has a hard time to offer, and something that still matters. It is also an asset that needs to be tightly integrated into the customers’ experiences. This asset can also prove to be a treasure trove of data on customer behaviour and –intention. Given this it can be argued that the ability to gather and work with data is nothing that sets an online retailer apart from their offline competition. On the contrary, in an app, AI, and Internet of Things world retailers can get an edge over online behemoths again – by providing a superior and holistic customer experience and a customer engagement, across touch points that online retailers cannot offer. Traditional retailers have far more possibilities, click and collect and personal service only being the simplest ones. Here we are back to the triple play of CRM, CEX, and CEM that I wrote about in a guest post for friend Paul Greenberg a short while ago. What does it take? Apart from making sure that store employees can dedicate quality time to their customers? What it takes is as a first step is using the data that is readily available in every retail company, data that is provided by the PoS, that is provided by the membership/loyalty program that might exist, by reactions to marketing campaigns, etc. And to analyse and use it. Even without predictive analytics and other advanced technologies it is possible to derive valuable data from it. Add a few simple survey questions, at relevant times, e.g. directly after the customer did a checkout, and improve experience and engagement from there. Especially smaller retailers can benefit from platforms like Epikonic, that provide an easy integration of loyalty system, PoS, CRM, campaigns and analytics, even beacon support, with experience and superior engagement. Key to getting, analysing and using this data are the concepts of value (to the customer) and, more importantly, relevance. Relevance is always in a context of needs, time, place. Let me use an example, a NZ men’s fashion retail chain. Ask me for a homewares retail chain or a department store I worked with, if you wish. The fashion retail chain is fairly upscale but not top end. I will not name it but rest assured that I bought quite some shirts there. They are doing well with varieties in good quality, an interesting web site and very forthcoming staff. They also have fairly upmarket store locations. And they know about me, should know what I bought where and when. They have my phone number and my e-mail address. I consequently regularly get an e-mail with the shirt of the week or another campaign which, frankly, goes away unread. They even recently did a survey that indicates a desire to find out where they could do better. Now, what could they do better?

The e-mails are not personalized but very product centric. Which also means that the offers are not personalized. So, in essence, they are doing mass marketing
I am passing by via one of their stores fairly regularly. After all there is a supermarket as well, where I often get some groceries

It is about offering value and being relevant. With the data that they have about me they do know that here is a person who seems to prefer not overly formal business wear. Fine. You know the style, colour, and size of shirts that I purchased. How about offering matching trousers, blazers, shoes? Or offering me a new seasonally adjusted combination, addressing me with my name, ideally along with a personal greeting of the store manager. This is not that difficult. A personalised offering would have far more potential to draw me into the store. This would be a start. Going on from there we can add simple technologies. The first two that come into mind are my mobile phone and beacons. Offer an app or, better, hook into an existing one that offers wallet functionality – maybe even payment functionality. This can be used for proximity- and hyper-real-time marketing. Make me a compelling, personalised, offer when I am nearby. With a PoS integration I could easily redeem it. Send me the receipt to the app along with a brief thank you note, or a very brief survey about my experience. Value, relevance, and convenience for me, valuable data for the retailer. Without a big cost outlay! Step the game up a notch. Add a beacon at the store entrance. Now you know who is entering, preferences, size, name. These are valuable information for the store personnel. Who does not want to be greeted by name? Add a few more beacons around the store and you easily know where customers walk, and where they stay. From here on we can get fancy. Offer some more information on the merchandise, e.g. by providing a QR code that I could scan or by directly scanning the product. Make me virtually wear a shirt using an electronic mirror – that might even help my wife giving her opinion fast. Marketing messages can get personalised by showing the clothes on my own shape, with my own face, given that I gave permission for it. This might even extend to the web site. Fancy? Yes! Possible? Of course! Necessary? Not yet, but likely soon.

Public-, Private-, Hybrid Cloud – Quo Vadis?

Mon, 11 Apr 2016 16:20:02 -0400

Back in 2012 thought leader Esteban Kolsky went through the Graphical Representation of the Cloud by Esteban Kolsky efforts of defining a pure, open cloud architecture with its three constituting layers: SaaS, PaaS, and IaaS, and samples of their interactions. A core focus lies on how an open cloud model solves the issues of security, scalability, and integration. Esteban also makes it abundantly clear that it can easily take 10 to 15 years to be widely adopted. First things first: He is right – at least to quite an extent. Personally I am of the opinion that any open cloud model necessarily includes private, and thus hybrid clouds. I say this while I agree that the panacea is the open model. Private and hybrid models are at least challenged in the security aspect and, to a lesser extent, in the integration challenge with the latter usually being mitigated by maintaining a white list of ‘approved’ applications, technologies, vendors, etc. Scalability shouldn’t be an issue for most companies, given that they work with a data center provider that is worth their salt. The bigger problem of security remains, but here one could argue that it is the same as for non-cloud, on-premise implementations. To a minimum, private and hybrid clouds have their value as transition steps. In reality it is more. Just take VW implementing a private cloud based upon OpenStack, delivered by Mirantis – which are, interestingly enough, not covered by Forrester Research in their Q1 2016 Wave on Private Cloud Software Suites. But I deviate …

Cloud Adoption

The recent RightScale report on the State of the Cloud tells us that nearly every company uses cloud of some sort. These may only be e-mail services, though. It also tells us that the use of private cloud went up drastically and far faster than the use of public cloud; and with it hybrid cloud, which is also a function of the widespread adoption of public cloud. Have integration between an app on the public cloud and one on the private sided and you are hybrid – and what business can work efficiently, or even effectively without integration? The State of the Cloud report also shows a slight reduction of multiple private- and single-public cloud strategies with a corresponding increase of multiple-public and single-private cloud strategies, with hybrid cloud strategy staying constant. The latter makes technology advisor Ben Kepes wonder in a recent computerworld post whether this is “an indication of greater move to the public cloud, or simply the last vestiges of conservative companies not willing to move to cloud of any flavor”. I’d like to add another possible interpretation in a second. Cloud models – wrongly so, I dare to say – are implemented to help companies doing things more efficiently, or to save cost. Why is this wrong? Because it is shortsighted! Without a further strategic view into business flexibility and business process improvement it will lead at best to short term cost savings and then it is missing out on the opportunity to de-clutter existing business processes and to improve them. The real benefits of a move into a cloud model come with revisiting and streamlining business processes as part of moving ‘legacy’ applications into the cloud. A hybrid cloud strategy helps achieving this in a stepwise approach, following the Think Big – Act Small approach that I described for Customer Experience, Customer Engagement and CRM earlier.

Public, Private or Hybrid Cloud? In the Fog

The preference of private cloud seems to be more pronounced in enterprises than in SMBs, which tend to have more of their IT in public clouds – yeah, plural as the average of productively used clouds is 3, with three more being experimented with. This number of used clouds again emphasizes the importance of integration although I, again, think that this is less of a (technical) problem and more of a logistics one. The problem that I rather see is that every major vendor intends to establish their platform as THE platform in an effort to maximize their own gains; which goes on cost of the customer benefit of cloud, I must add. This is an attempt that is obviously doomed to fail, not only on a PaaS, but also on an infrastructure level. We will not see all businesses around dumping their Salesforce implementations in favor of SAP, Oracle, or Microsoft (take any other permutation of names here); nor will we see SAP develop an e-mail system that can seriously compete with Google’s. Neither will we see AWS emerging as the sole surviving infrastructure … We will (continue to) see businesses focusing on value delivered, which will heat up the competition and will have a healthy impact on pricing. For a starter: Many to most businesses will not rely on a single cloud – as they did not rely on a single business applications vendor in the past. And this situation gets even more complicated by adding productivity- and other applications into the mix. The resulting need for integration is already taken up. There is a plethora of cloud-based integration platforms available, some of them open, some of them proprietary. Going forward the selection of an integration platform will become even more important than the selection of an applications platform. Though one can argue that most applications platforms deliver integration capability, as it stands these integration capabilities work best intra-platform and not inter-platform. So where are we headed? Public and Private Clouds are here to stay. The smarter of the more conservative companies and the slower adopters will likely use a transition model that involves private and hybrid clouds to improve their business. Businesses will use multiple clouds on different (PaaS, IaaS) platforms, which will put more emphasis on another IaaS – Integration as a Service.

CRM Evolution - Some not so random Thoughts

Fri, 21 Aug 2015 22:07:47 -0400

CRM Evolution 2015 was a very vibrant conference with lots of discussion that included a number of high profile industry influencers. For me as a first time attendee it was amazing how approachable many of these people are. But then this might come with the territory.

To understand these takeaways it is important to know that my reason for attending was getting into closer touch with what is going on in the CRM world outside SAP - and New Zealand. So, these are purely notes and thoughts that result from sessions, discussions with influencers, speakers and other conference attendees, and not learnings from vendor briefings. Also, this event was split into three separate conferences:

CRM Evolution
Customer Service Experience
Speechtek

I nearly exclusively concentrated on CRMEvolution and one session from the Customer Service Experience.

First things first: Was it worthwhile coming all the way from NZ? This is very clearly a yes. Paul Greenberg and the team did an amazing job in lining up interesting speakers.

What now are the topics that currently seem to move the industry in random order?

Customer engagement (CEM), customer experience (CEX), customer journey (CJ) and how these topics relate to CRM
Big Data, with a view on the Internet of Things IoT, and related to it: Predictive analytics
How to do things CRM right
Not surprisingly: The Future of CRM (technology as well as industry)

Also not surprisingly these are all interrelated.

CEM, CEX, CJ and CRM

The intersection of these three topics is extremely interesting. These are also controversially discussed topics. Paul Greenberg recently, in another “stake in the ground” article, gave current definitions of CRM, CEX, CRM, together with good rationales. I would like to not dive too deep into this topic for the moment and think that I will write down some more thoughts of my own soon. Only so much: A good customer experience and positive customer engagement rely heavily on relevancy. Relevancy is increased by addressing a customer/prospect with the right communication at the right time, using the right communication channel as well as the ability to consistently do it across all channels. I would call this channel agnostic, rather than omni channel.

Customer Journeys and their mapping are topics that still evolve, both from a vendor point of view but also looking at what analysts say. Of course customer journeys are handled differently in B2B and B2C scenarios. Mitch Lieberman presented the Sugar CRM implementation in a B2B scenario, which reminded me of the cross of a buying centre with an action plan, including predefined actions and communications channels for these actions that suit the corresponding stakeholder and the information. The idea is that a customer engagement happens best using the touch points suiting the customer representative best.

On the B2C side things are quite similar. Ray Wang sees customer journeys being mapped “intent driven”. This does basically mean to use all available data (i.e. Big Data, to use the buzz word) to figure out a user’s intentions and to construct touch points in a way that these intentions are already taken into account. This sounds like magic but is started to be put in place, e.g. in support scenarios. An example that came up in a later session is a telco that uses data as different as that gathered through a user’s web search activity, the status of her web router, browser, other users’ experiences, etc, to prioritise and suggest solutions - and this consistently through the diverse support channels.

This directly brings us to the next group of topics.

Big Data, Internet of Things, and Predictive Analytics

Big Data is probably last year’s big thing but the term still brings a point across. All of us are generating incredible amounts of data, structured as well as unstructured. One of my customers for example, a not so big retailer, sits on several tera byte of sales data, a treasure trove for targeted marketing. The amount of data is growing fast and thanks to this year’s buzzword: Internet of Things, which describes connected sensors that interact with each other, we with them and they with us, the pace of this growth will even explode. This amount and growth of data continues to be a challenge, although technology meanwhile allows intelligent near real time analysis of huge amounts of data. The good news is that IoT is creating structured data, but then all data that is generated via social media, including sentiments, is unstructured.

This is where Predictive Analytics, or Intent Analytics, kick in to support businesses in their customer engagement and CRM processes, and providing a satisfactory customer experience, which brings us back to the previous section. The telco in the example above uses the huge amounts of data that are generated via their touch points and applies predictive analytics “algorithms” on them to be able to help customers in case of a problem. This help will be offered via channels that are as different as chat, voice, IVR, the company web site. Of course this help also relies on a very good integration into their internal knowledge base that gets constantly updated so that the relevance of articles can get determined based upon all available data.

Other applications of Big Data- and Predictive Analytics are Oracle’s release of a social analytics engine that helps determining the priority of calls for help on social media (apparently based on the number of followers, e.g, who cries loudest gets better support …) or an energy grid provider’s ability to constantly analyse the status of its network with the ability to predict failures up to 8 days in advance. This is incredible useful for preventive action and the optimal planning of maintenance.

But back to the Internet of Things. We all are starting to carry around an increasing number of sensors. These sensors interact with us, we with them, and increasingly they with each other. It also leads to the development of platforms and APIs between platforms that increase the range of services that businesses can provide. Think of decades old scenarios like the your fridge ordering a refill of butter because butter is going to run short soon, or the current experiment of Amazon Dash, that essentially is a button that gets hooked up to your wifi network and does exactly one action: It orders a set product, which then gets delivered right to your house - maybe via a drone. Just be sure that the button is out of reach of your kids, or else you might get more washing powder than you can use in the next decade.

This interaction of sensors in combination with platform integration will allow the creation of services and experiences that we currently can only partly imagine. But I do not think that anyone has a clear vision of where this will lead, who will benefit of it (except the platform providers), how privacy can get maintained and what regulatory requirements are coming up.

CRM Done Right

This is a kind of eternal topic. Theoretically it is not that difficult to apply a number of seemingly common sense principles. Of course I need to acknowledge that every business and organisation has some constraints. Still it is really surprising - at least for me - how often simple principles like

start with an end in mind
iterate, have goals use small steps
write it down and make it measurable
foster an appropriate corporate culture
get your processes straight
provide the employees with what they need. CRM systems are not only for the management

are not applied. These are only the ones that were referred to most commonly at CRM Evolution but I think they paint a fairly complete picture.

Instead we still seem to see technically driven implementations that are intended to support management with more reporting and control capability and that do not yield positive results and lead to dissatisfaction. I think that this gets increasingly understood as being a problem, by businesses as well as by vendors, which is evidenced by user interfaces of these business systems getting more and more consumer grade and by an increasing ability of the software stacks to support little, targeted applets, either native ones or ones that are plugged in via an integration layer like REST.

The vendors getting their act done leaves the businesses in the hot seat. Without a strategy, a customer focused corporate structure and culture, and an implementation plan that is flexible enough to be regularly adapted to follow changing realities CRM implementations will continue to fail.

At this point let me bang my head against a wall once more: To me it still seems that CRM is a strategy, regardless whether the market has “agreed” upon calling it a technology. Technology doesn’t help. It is a tool, not more.

But now let us have a look into the Glass Ball.

The Future of CRM

CRM was pronounced dead by many pundits - multiple times. But it is still around and it is here to stay.

CRM evolved and it will continue to evolve. But what are the next evolution steps? What are the main drivers?

Main drivers that I see are

an increasing need for real time decision making
the necessity for companies to identify signals in a world with a lot of noise
the necessity for companies to be relevant to their customers, to stand out in a world that has a lot of noise

I think that these will result in some main lines that will govern the next steps of this evolution:

in the shorter term best-of-breed will continue to get stronger; we see it already now with many departmental and point implementations, many of them thanks to the cloud. In this respect we have gone full circle since the beginning of the nineties, from point implementations to suites, and back. This will be followed by
platforms with open APIs, rather than suites. Platforms and ecosystems are already there. What is not yet there is real platform interoperability. This is still project work. In the future this should lead us to a world where business objects and -services that are provided by different providers can get easily combined to assemble end-to-end processes - or, hopefully not, to a world that has only one remaining platform. Hasso Plattner formulated this thought of free combination of business objects to processes at the end of the nineties (OK, limited to SAP products) and had SAP Business by Design built to follow this thought; these days Bob Stutz formulates it fully generic. And he is right. The CxM market is more than big enough.
very, and I mean VERY, strong analytics capabilities that are directly (and automatically) actionable, driven by AI systems. These analytics capabilities support superior customer experience and superior customer engagement, which forms a kind of relationship between customer and business, whether the customer wants it or not. The customer engagement will be pre- and post-sale, always with the intention of making the next sale (the current buzz for this is “service is the new marketing”). It will happen on the “channel” that the customer prefers and have and use enough contextual information to provide relevant information, at the right time and place. To achieve this the analytics engines will be fed by both, structured and unstructured data, fed by numerous sensors and devices of sorts that we can only start to imagine now
customer engagement will first become channel agnostic and then, depending on how IoT develops, device centric. This is where it gets really fuzzy. We just don’t know enough about this yet.