aheadCRM - Blog #predictive analytics

Clari - Nipping at Salesforce's Heels?

Mon, 26 Mar 2018 06:41:08 -0400

A brief while ago I had the chance of talking to Andy Byrne, CEO of Clari, about how AI can help making sales organizations more effective and efficient. Clari is a vendor of Opportunity-to-Close solutions. G2Crowd lists the company amongst the leaders of its Sales Analytics Software quadrant, while Gartner Group in 2017 named it a cool vendor in the Tech Go-To-Market. Shortly after the conversation Clari announced the closure of a $35 Million funding round “following record growth”, essentially a tripling of their customer base while maintaining a near 100 per cent renewal rate. According to Andy, the company applies “machine learning focused on sales”, i.e. predictive and prescriptive analytics to improve pipeline visibility and to get more insight into which opportunities are more likely to close than others. This helps in focusing on these opportunities. This solution was developed after having in depth conversation with a number of big sales teams, figuring out their challenges/problems. As a result of this the company is addressing three problems.

Many to most sales reps do not consider CRM systems (or SFA systems, for that matter) as particularly helpful.
Sales managers do have a poor visibility into what their teams are doing, with which opportunities they spend their time.
Executives and Sales Operations are dealing with “XLS hell” because the system’s forecasting ability is broken.

All in all, points two and three are consequences of point one. If a system is not of help it is a time-waster and tends to be avoided. Data about opportunities will not be entered in a timely manner nor will it be very accurate. Clari’s solution to this is to help the sales reps by taking the chores out of the process by tying other systems into the opportunity management process. These systems include Gmail and Exchange for email and calendaring, but also attachments. Further connections to marketing information systems (e.g. Marketo,), Docusign, Xactly, ClearSlide, and others are in the pipeline. Data coming from all these sources is used to feed Clari’s prediction- and prescription engine to score the opportunities as well as suggest activities for opportunities. That way, Clari wants to improve sales reps’ productivity, while giving managers insight and the chance of coaching their teams – all with the goal of increasing the conversion rate. At this moment Clari integrates with Salesforce, but plans to integrate to other CRM/SFA systems, too. The approach is to not position itself as a competition but to augment and enhance the user experience by “marrying AI with beautiful design”.

MyPoV and Analysis

Having seen the capabilities and the UI of Clari, I came away duly impressed. Clari offers a clean user interface that helps people in instantly grasping the status and risk of the pipeline as well as the individual opportunities along with the activities going on to pursue them. Opportunity Analysis with Clari And, looking at Salesforce being the main system to integrate into, this is quite a proposition, especially as Salesforce’s Einstein is still quite tied to Salesforce data. However, Salesforce is addressing exactly the challenges that Clari looks at. The company recently announced corresponding improvements to Sales Cloud Einstein, the Salesforce, Inbox and improved sales analytics. And the recent acquisitions of Cloudcraze and especially Mulesoft will improve Salesforce’s ability of tapping into very relevant data, which improves the company’s position. The same holds true for Microsoft and SAP Hybris. Both solutions also offer opportunity-scoring solutions based upon machine learning. In the case of Microsoft already since late 2016. Both solutions take in data from various sources, too, although the link to past sales rep activities is less pronounced. Still, all three vendors, Microsoft, Salesforce, and SAP, offer activity recommendations targeted at optimizing the chances to successfully close opportunities. And there are not only doing it for opportunities but also for leads and other entities. Still, the VC community is upbeat about Clari, as evidenced by the successful recent funding round.

The Cautions

While Clari seems to have a strong Opportunity-to-Closure solution this laser focus also puts the company into a niche that the big vendors will close themselves, if not as good as Clari does, but still ‘good enough’. And they are on it. Personally I do not see Clari as an acquisition target of one of the big 4 (yet). Further, there are not yet many partnerships with CRM vendors. Salesforce is a good starting place, but it is exactly that: A start. And Salesforce is known for usurping spaces that are owned by partners, if it sees an opportunity (this is not only true for Salesforce, of course). For Clari this means that the company needs to do four things

Stay ahead of the competition from an output point of view. Integrating with Clari must show clear and measurable benefits for customers using a CRM system which already offers similar functionality
Go SMB. This is a largely underserved market that is still very fragmented and mainly covered by vendors that do not have the scale to offer wide solutions
Partner, partner, partner with vendors of CRM solutions, to gain broader exposure
Based upon the current offering, build complementary solutions that solve real business problems

And all this while staying at an attractive price point. All in all, Clari offers a solution that enterprises and SMBs should look at.

SAS Customer Intelligence 360 - Turn Data into Experience

Tue, 20 Jun 2017 10:00:34 -0400

A while ago Angela Lipscomb from SAS got in touch with me to get me introduced to SAS’s concept of a Customer Decision Hub. Their Customer Decision Hub is a solution concept that shall allow organizations to derive insights and to trigger actions from interactions with external parties, like customers based upon rules and the derived insights. A Customer Decision Hub e.g. orchestrates the determination of Next Best Actions, and allows responding to an incoming request in real time using analysis and decision logic. At the same time standard communications can get suppressed based upon the same set of rules. In other words, the Customer Decision Hub fosters customer engagement based upon inbound signals that get analyzed and processed through the organization. Why is this remarkable, I hear you asking? It is remarkable because SAS Software first of all is an analytics company with a strong reputation for enterprise analytics at the higher end of performance and price point. SAS describes itself on LinkedIn as “the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world the power to know®.” SAS is not a company that is widely known for being actively engaged in the customer engagement market (pun intended). So I was intrigued. And so should you be. Finally, a few days ago my somewhat erratic schedule allowed me to have a follow-up with Troy Kusabs of SAS Software in NZ, something that he offered to do earlier. Troy gave me some more insight into the concept and how SAS software does support filling it with life. It bases on the SAS Digital Intelligence and Personalization platform “SAS Customer Intelligence 360”. The purpose of SAS Customer Intelligence 360 is to allow businesses the creation of relevant customer engagements, based upon data, which result in better customer experience. SAS dubs it as “create relevant, satisfying, valued customer experiences”. SAS Customer Intelligence 360 consists of two applications that sit on top of the SAS analytics system and support marketing by enabling functionalities for real time decisions, intelligent marketing and campaign management. These applications are named SAS 360 Discover and SAS 360 Engage, which allow for collecting data from digital interactions, to gain insight out of these interactions, and then use this insight to meaningfully engage with customers across web, chat, e-mail, and mobile apps. One can roughly say that SAS 360 Discover feeds the analytics engine and that SAS 360 Engage uses the analytics results. Businesses can define and maintain data collection and normalization rules and, based upon these, assign personalization rules. Customer interactions get tracked using a simple enhancement of e.g. the web site or app code, which helps to build their profiles, first anonymous ones, where possible identifying and merging those technical profiles. This data gets aggregated in a data mart and can get further enriched with data that comes from other sources that the business has, like product information and information out of the CRM-, and other systems. This information then can get used for further engagement using the core strength of SAS, which is the strong analytics system. This engagement is the job of the SAS 360 Engage application, which allows to combine messages and assets to marketing tasks, which get aggregated to customer journeys, called activities by SAS. This, again, is supported by SAS analytics capabilities, including predictive analytics and machine learning. The overall system runs on AWS and is mandatorily designed as an open platform. Connectivity to the source systems is given by APIs and ETL functionalities.

My Take

It is good to see a traditional analytics vendor stepping up and helping their customers to build an integrated solution that allows them to take advantage of the treasure trove of data they are sitting upon. Customer engagement and customer experience being some of the hottest topics around make for a good showcase of this ability. Of course SAS offers solutions for decision management, fraud detection, risk management, too. SAS makes a pretty compelling case by its ability to combine a, if not the, leading analytics engine with business logic. Analytics is a means to an end – a business end. This case is supported by an API approach and the statement of offering an open platform. Having said this, the market that is covered by SAS Customer Intelligence 360 is a very competitive one. Business applications vendors like SAP, Microsoft, Oracle, and Salesforce – or Adobe – are trying to corner it, too. They might have less powerful analytics engines but they command a lot of the business logic, and the business knowledge. Then we have specialty vendors, of which I want to mention only Kitewheel and Thunderhead here. These companies excel in the disciplines of discovery and engagement and have analytics engines that are geared towards supporting their specialization, and they are offering out-of-the-box (OOB) integrations into major business- predominantly CRM systems. Integration is an important topic. While offering APIs is key the message of having OOB integrations is very powerful. Lastly, it is about messaging and philosophy. Looking at the ‘get started’ info graphic is telling here. The thinking is company centric, and not customer centric. With that, it needlessly limits itself. While there are mentions of the customer being ‘fickle’ it assumes that the customer journey can get pre-planned by the company, which is wrong. The company can offer a many of touch points, out of which the customer chooses the ones (s)he finds most convenient at any given point in time. The messaging sincerely is about talking to the customer instead of talking with the customer. Changing the messaging to an outside-in viewpoint and then further improving the solution from there could help SAS really stand out.

Gartner MQ BI and Analytics Platforms - Lots of Movement

Wed, 22 Feb 2017 16:43:45 -0500

Last week Gartner published the updated version of its Magic Quadrant for Business Intelligence and Analytics Platforms, and I need to say that there has been a lot of movement in both directions, up as well as down. There has been a lot of reshuffling especially in the Visionaries quadrant. This can partly be attributed to a changing market that caused Gartner to combine a few of last year’s assessment criteria as well as adding two more critical criteria as below: Critical Capabilities Dropped or Changed:

Combined BI Platform Administration with Security and User Administration
Modified Data Source Connectivity to Data Source Connectivity and Ingestion
Combined Publish Analytics Content and Collaboration and Social BI to Publish, Share and Collaborate on Analytic Content
Added Visual Appeal to Ease of Use

Capabilities Added:

Smart Data Discovery
Platform Capabilities Workflow Integration

Smart Data Discovery emphasizes the increasing importance of AI and machine learning as part of analytics systems. Gartner defines it around the automatically “finding, visualizing and narrating of important findings such as correlations, exceptions, clusters, links and predictions in data that are relevant to users without requiring them to build models or write algorithms. Users explore data via visualizations, natural-language-generated narration, search and natural-language query technologies”. Workflow Integration acknowledges that there is no actionable insight if there is a standalone analytics system. It is defined around the number of products “needed to deliver the critical capabilities and the degree of seamless integration and workflow between capabilities/components”. This has been true for a long time, but hey, better late than never. Gartner itself states that the changes have been major and that therefor one should not do a year-over-year comparison. Still I will venture into this territory, suspecting that the two additions favor bigger vendors. Notable changes to last year’s quadrant include

A drastic advancement of Microsoft and Tableau making them, especially Microsoft, the undisputed leaders of the pack
Salesforce and Sisense jumped big time from the Niche Players into the Visionaries quadrant
Clearstory Data moved up in terms of Completeness of Vision while TIBCO Software and IBM moved up on the Ability to Execute axis
Alteryx, Pentaho and Logi Analytics dropped off the Visionaries quadrant and became Niche Players
With Zoomdata we see a new player in the Visionaries Quadrant
We are welcoming Oracle back in the Quadrant as a Niche Player

There has been a little movement up or down for SAP (slightly up on either axis), SAS (slightly up on vision but down on execution), Qlik (slightly down on vision, marginally down on execution) In a bit more detail, focusing on movements in the leaders and Visionaries quadrant.

Microsoft

Microsoft offers a clear and visionary roadmap, underpinned by monthly delivery to it, on an already very strong product that is powered by Azure. For on premise usage there is the Power BI Desktop solution. However, the strong focus on cloud (which I think is right) along with still some lacking functionality hampers the execution score. This is something that a development powerhouse like Microsoft should be able to address. Consequent execution on their roadmap along with a continued price war should make them a formidable competitor for Tableau, which Microsoft is likely to surpass in the coming year.

Tableau

Tableau is in some sense the gold standard of analytics. The software is very interactive and intuitive and got enriched by lots of Enterprise functionality, likely in an attempt to fend off Microsoft. As a caution Gartner mentions that these new functionalities appear to be work in progress. Tableau appears to be in a challenging position with the need of redefining their differentiators (interactivity and inductivity are degrading as differentiators) and the parallel need to invest into more up-and-coming functionalities like smart data discovery. Compared to Microsoft Tableau is also expensive. This year will show how Tableau manages this tight spot.

Qlik

Qlik has a sound and robust set of product but seems to have a challenge supporting them all. The company being taken private customers seem to have a bit of concern regarding the stability of the roadmap, which so far seems to be unjustified. The main issue that Gartner sees with Qlik is a lacking investment into smart data discovery, thus the downgrade in vision.

Salesforce

Salesforce jumped far into the Visionaries quadrant because of the combination of AI enabled analytics (Einstein), interactive visualizations (Wave) and the purchase of smart data discovery startup BeyondCore (which consequently vanished from the quadrant). Salesforce seems to concentrate on their installed base when marketing and selling their Analytics Cloud. Deeper integration of above mentioned functionalities in combination with increased marketing and sales to new customers could move Salesforce into the Leaders quadrant.

Clearstory Data

Clearstory Data has been in the Visionary quadrant last year and moved strongly on the vision scale this year. This is largely due to a strong understanding of their market, ease of use, the ability to do complex analytics and a roadmap that focuses on the right things (smart data discovery). Clearstory will need to work on their being fairly unknown in order to enjoy continued success.

IBM

IBM got propelled up on the execution scale by the strong market presence of Watson that delivers on AI driven, machine learning, analytics capabilities and smart data discovery – ticking all the boxes. IBM’s ‘problem’ is that they also have Cognos Analytics, which somewhat jumbles the vision. Further, IBM surprisingly seems to have a challenge with higher data volumes.

Sisense

Sisense moved up from the Niche Players into the Visionaries due to a strong focus on smart data discovery and embracing innovative technologies like voice query and offering ‘analytics bots’. Being a young company they lack some advanced capabilities. They also cannot yet show big deployments.

TIBCO Software

TIBCO Software renewed their focus on critical parts of analytics software, notably smart data discovery, albeit they are not yet ‘cloud’ enough. The renewed focus along with more emphasis on customer engagement helped them up on the execution scale.

Zoomdata

Zoomdata debuts in this quadrant based upon their strong focus on stream analytics. They are strong in these areas, embed well but lack functionality and customer engagement (and customers). They also still need to prove their ability to support big numbers of users.

But wait, that cannot be all!

What about other powerhouses like SAS, SAP, and Oracle? Well, Oracle is back on the map after refreshing their analytics offering. Oracle has gained traction and with a roadmap that includes machine learning, smart data discovery and NLP stands a chance of moving up to the Visionaries quadrant next year. SAP is virtually unmoved, slightly up on both scales probably. The analytics offering seems to have good momentum and the roadmap is geared into the right direction. The company’s main challenge continues to be support and the complexity of SAP product, both being actively targeted. This leaves us with SAS. While SAS has one of the strongest functional footprints and strong integration. Similar to SAP, SAS hurts itself in cost, ease of use and ease of making business with. On top of this there seems to be confusion about the different product lines SAS offers. As with SAP none of these challenges should be insurmountable.

Customer Experience - It is all in the Data. Really?

Thu, 13 Oct 2016 14:48:17 -0400

A while after my earlier discussion with Abinash Tripathy from Helpshift about the value for customer experience of bots in customer support he contacted me with some exciting news about what he and his team are doing now. Believe me, it is interesting – but read for yourself. Our conversation, of course, led on to another vivid discussion about things to come and things that in our opinions should come. The bottom line is that we live in a data driven, always on, real-time world, where prediction of events or the ability to suggest an action is becoming increasingly a differentiator … be it in a B2B- or a B2C world. Think of Rolls-Royce selling uptime of their engines, entire airplanes nearly continuously sending telemetry data “home”, or the massive amount of data that a Formula 1 car continually sends in order for the team to take proper real-time decisions. Any car already collects a lot of data – it just needs to get connected to allow for prediction of maintenance to prevent failures. Or think of entire power grids that are already instrumented in a way that allows the operator to predict a failure several days in advance, so that the affected element can get fixed before it fails.

It is all in the data?

The secret is in having the data. And in the algorithms, be they event- and rule based, or more sophisticated and using machine- or deep learning. Neither data nor algorithms alone are the goal. Because what is needed is actionable insight. Actionable insight emerges only if the right algorithms are applied to the right data. Data and algorithms are the means to an end, and the end is a job-to-be-done. The job-to-be-done in our support context is a prediction, or a suggestion, or just a notification. But how does the customer benefit from it? Elementary, my dear Watson! A system that works like this offers an improved customer experience by avoiding failures and by offering the ability to pro-actively approach customers to get things done. Combining this with ubiquitous mobile communication it also offers the possibility to engage with customer in their context, meaning right situation, right time, right location, right communication channel. Or, formulating it the other way round: Massive computing power combined with sensors, delivered via the cloud to mobile devices, is what enables the delivery of real-time, data driven, and personalized experiences. Both, customer support and –engagement, are increasingly delivered in app or through messaging interfaces. Despite the decreasing number of apps that are getting installed, the offer of good, immediate support may well be the incentive for customers to install a branded app, especially if the backend can already work with enough data to establish the relevant context, automatically and manually delivered by the customers via their devices.

The big bright IoT- and AI future

Now by extension this works in IoT scenarios, too. As said before, the main juice lies in the data and the algorithms on the back end. Imagine a connected car that tells its owner that something critical is about to break and probably also makes a service technician initiate a call to offer on-the-spot support. There are lots of scenarios possible, including the currently hotly contested and wild-west like home automation market or multiple scenarios in predictive machine maintenance. In an –as-a-service economy most product vendors need to change their business models from selling machinery to selling outcomes. The product is only a vehicle to achieve the outcome, nothing more. The customer does not want to have a washing machine but clean clothes, not a light bulb but light, not a car but (individual) transportation (yes there are some not fully rational parameters involved, but in essence it is about the outcome). Mainly these scenarios are still in a fluid stage, but they are emerging. Technologies are being built. Ecosystems are developing around supporting them. Think of home automation and IoT scenarios where all the big names, Amazon, Google, Apple, not talking about Telcos, and others, are trying to establish themselves, offering platforms for other companies to connect devices to have them talk to each other and a control hub. Wild West, Gold Rush style, as said. But as a vendor one needs to position oneself already now and select the right ecosystems to partake in. As a word of caution: During Gold Rush times it was not the customer who benefitted most. This would be an article of its own …

But what about now?

So, looking more near term. Contextual, relevant marketing messages are best delivered based on rich customer data, too. Delivered to the right customer, at the right time and location, in the right context – and using the right, i.e. the customer’s preferred, channel. This is a good part of the secret behind companies like Krux, or Kahuna, to name but two that have been talked about lately. An increasingly important part of this the ability to deliver messages in-app, via messaging systems or as push notifications. However, the good ole e-mail is not dead, too; and it is likely to stay for some more time. Another important part is the ability to build segmentation models that get continuously evaluated in real time, over a period of time. This enables the event-based initiation of a communication, e.g. when entering a geo-fenced area during a certain time, perhaps including weather information and definitely information about the person. Add beacon support and/or integration to e.g. payment systems, loyalty systems, survey systems and a whole lot of near term opportunity arise that can lead to service automation tying into marketing automation, which in turn directly results in a sale, thus somewhat short-circuiting the traditional distribution into three functions, marketing, sales, service. It also leads into the direction of the big, bright future. Roadblocks included. Many of them. Exciting times ahead.

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.

Customer Experience and Design

Mon, 18 Apr 2016 16:02:32 -0400

Love Gears, FreeImages/deafstar A brief while ago I had a talk with Ian Hodge, a very established Australia based consultant. One of the topics we talked about was what customer experience is and how to measure it. This led to following brief conversation on LinkedIn.

Hi Ian, you told me that you are thinking about what customer experience is about. Have a look at below article by Paul Greenberg. The main topic is something else but in the second half there are some very good thoughts. http://www.zdnet.com/article/adobe-doubles-down-but-it-cant-stay-in-vegas/ Cheers Thomas
Thx Thomas - interesting. I like the "catch 22" problem that delighting a customer runs the risk of increasing expectations etc. An interesting challenge I perceive is the integration of data analysis with creativity in design. This was always an important aspect of "marketing" and now applies more intensively and broadly with designing "customer experiences". Look forward to keeping in touch. Rgds Ian
yes, this challenge is what I tried to voice. It is not always a technology approach. But then once could have the idea of using marketing approaches to it - at least in a B2C world something like the following could work in the digital world: Try different approaches (designs) and roll them out to different target groups. Let it run parallel to current state while gathering data. This can be usages, usage patterns, abandoned carts, changes to cross-, upselling behaviour, recommendations, endorsements, etc. Apparently this needs to be planned.
bugger - hitting enter to send should be forbidden ;-) Combine this with sentiment analysis and all of the sudden you have an approach that can cover both: The consumable experience and the general experience that Paul mentions. I tend to look at these to as two cycles, an inner one (consumable experience) and an outer one (general experience). The inner one influences the outer one, probably even drives it. Cheers Thomas maybe I should write a short blog post using these thoughts ;-)

How does design and creativity factor in? A very interesting question! But first: What is customer experience at all? And let’s assume that positive customer experiences are important for businesses and brands, to reduce churn, increase customer satisfaction, facilitate customer acquisition, company differentiation, etc.

Wikipedia defines customer experience as “the product of an interaction between an organisation and a customer over the duration of their relationship. This interaction includes a customer’s attraction awareness, discovery, cultivation, advocacy and purchase and use of a service”. According to the business dictionary customer experience is “the entirety of the interactions a customer has with a company and its products. Understanding the customer experience is an integral part of customer relationship management. The overall experience reflects how the customer feels about the company and its offerings”. Harley Manning from Forrester Research sums it up to “How customers perceive their interactions with your company”. I like Manning’s definition. It is short and crisp – and clearly shows the difficulty of whole industries: How to measure it? And on a more basic level: What is a touch point? Let’s start by answering that one. Google comes up with this definition: “A point of contact or interaction, especially between a business and its customers or consumers”. What the above definitions of customer experience are implying is that there is an inner loop and an outer loop of customer experience. The inner loop being the individual experience at every touch point, something that we can call the consumable experience, to use a term that Paul Greenberg coined. The outer loop I would call general experience. It is the overall impression of a business. General experience is also influenced by a third dimension of experience; let me call it indirect experience, lacking a better term. Indirect experiences are all experiences about a business or brand that we gather while interacting with another brand or company. For example competitive advertisement, a friend’s recommendation, hearsay, something that comes up on my Twitter or Facebook feed, … A point in case of their existence is the way social customer service currently works: Companies tend to work on complaints coming from people with larger numbers of followers – aka influencers. While this is not illegitimate it is nothing more but the high-tech implementation of the old concept of supporting the loudest cry first. One now could argue that indirect experiences come through touch points, too, but then these are widely outside the control of the company or brand. Therefore I’d like to keep them separately. General experience is created by the sum of all consumed and indirect experiences and then develops a life of its own. General experience influences how future consumable experiences are perceived, especially if it is negative. As the above definitions agree an experience is something the person perceives – something intrinsic to the person. And not everything in our world is digital – luckily there still is human interaction; and there are physical things, too. Customers and prospects experience companies and brands physically, digitally, and by interacting with other humans. Not only the usability of the web page – or its mobile-friendliness – products and solutions, but also the location and layout of the store matter; the manner, attitude, helpfulness of staff, even their clothing! The usefulness of the offered solution, expert advice, support, if needed. All these are, in current lingo, potential touch points with a company, and their individual sequencing form a customer journey. In a guest post on friend Paul Greenberg’s ZDNet blog I wrote

"One can argue that there is no experience without engagement. Engagement often can be managed but essentially creates data (think Internet of Things), data also about the quality of a customer's experience. This data then can be used for improved engagements and thus experiences.”

The challenge is fairly obvious. While one can easily manage and measure digital interactions, the impact of design is a bit harder to get. This is especially true in a real-time world. Touch points resemble a menu of possible interactions. Each of these interactions makes sense in one or more parts of the overall ‘customer journey’ from identifying a need to retiring the solution to this need. The menu part is essential here: Customers’ individual ‘journeys’ are different and customers will choose their interactions and pace of progress themselves. Businesses are well advised to not force their customers on strict paths! The business offers touch points, the customers choose the ones that suit them at any given time and place. And customers expect the result of interactions at previous touch points are considered in the process. They also expect the business not being ‘creepy’. If touch points are designed and built in a way that they tie into each other and provide relevant data (to both, customer and business) the customer experience can be measured. At least indirectly. The measuring part is fairly straightforward in the digital world. Not so in the offline world. Or how do you measure the experience that my daughter has when she plays with her new toy for the first time? Things get a little easier – and scarier if you think it to an end – in an Internet of Things world, though. But until all things are connected and call back home, and humans are fully wired there are less intimidating possibilities. Usages and their patterns can be identified, abandoned carts, reactions to cross- and up-selling attempts, purchasing history, campaign reactions, price sensitivity, recommendations, endorsements, customer movements in a store and outside, usage of things can get analysed using sensors … All these and much, much more can get measured and evaluated. And then we still have surveys and questionnaires … Add sentiment analysis and predictive (intent driven) analytics including machine learning, to the mix and there is a very powerful toolkit at hand. Don’t get me wrong. Setting this up is a daunting task that requires quite some strategy and planning. Else one will end up with a heap of data; data that can hardly be translated to information and knowledge to be acted upon. But I hear you wondering: How do creativity and design fit in here? Can this be measured? It can, to some extent. There are guiding principles, evolving, for sure, that indicate how to design and offer good easy-to-use interactions. Being guiding principles they can be implemented in different ways. Like marketing campaigns one or more of these designs can get field-tested using target groups of customers. This approach also gives an approach to the eternal truth that not every customer is made equal to a business. Combine this with a Voice of the Customer program and data will be gathered that allows the derivation of customer experience. So far this is all technology. Technology in itself does not help but is a means to an end – an enabler. Implementing technology will be just a waste of effort and money if it does not follow a strategy. This means that customer experience must be implemented from the very top and be part of every IT- and business initiative. And yes, making customer experience a priority could even mean adjustments in the overall business model. From a customer facing point of view this means:

Identify your customer segments and formulate a value proposition that enhances their experience
Deliver
Measure and repeat

And here we are back to my model of thinking big while acting small.

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.