Taking credit for the sunrise
If you read a lot like me, you might notice almost daily there’s a new study that contradicts some earlier research. Something causes cancer then it’s good for you. You know the drill. What’s going on here? Do we simply not know what our research is saying? Can nobody correctly interpret the data? None of this would mean much to CRM except that with the advance of big data and analytics, the front office, i.e. the relationship between vendors and customers, is coming to resemble many other endeavors that rely on data analysis. Here is my take on all of this.
Very often the research we get in the popular press and in business interactions represents the findings of correlation studies. Simply put correlation tells how strongly two events are to one another and it takes some sophistication to understand.
We can think of correlation as probability but we need to understand what it means. A coin toss has a 50/50 chance of coming up heads or tails (.5 probability). So 50% is exactly neutral. If something had a 30 or 40 percent chance of happening, it would be negatively correlated. In other words, the probability of something not happening would be greater. However, a 30 or 40 percent chance of something happening is not zero which is why we still get rain on days when there’s less than a 50 percent chance of it.
So, a probability of greater than 50 percent is what we’re usually looking for and the higher the number the better the correlation. A 90 percent probability is interesting but 60 or 70 percent—not so much for reasons that are obvious by now. Still a 90 percent correlation is not a sure thing and using the weather analogy, we sometimes see sunny days when rain has a 90 percent chance of occurring.
In business, we’re beginning to use correlation a lot but that disappoints many because correlation alone won’t tell us another important part of the story, causation.
Causation is the reason behind the correlation. It’s the data that, added to the correlation data, will provide the necessary information on which to make a decision. So, for example, a sales person evaluating prospects might look for high correlation between a prospect’s need profile and the vendor’s solution. That’s a good start but it’s missing something very important. It says nothing about the prospect’s motivation which might only be found through more traditional means like making a sales call.
What? Correlation isn’t enough? Consider this—at the correlation level a prospect in need of a solution looks just the same as one that just bought something from your competitor. Causation in this case is another word for a buy signal and if you look at buy signals and not just correlation a customer that just bought will look very different in this one dimension than one still looking.
In sales and marketing analytics we’re mostly focused on correlation and that means we’re far from foolproof in our predictions. I am not trying to get on anyone’s case but the fact that we’re so vested in correlation simply tells us where we are in the lifecycle of analytics as applied to CRM—there’s more work to do.
Another way to look at the situation is through the lens of qualitative vs. quantitative data. So far I’ve been focused on quantitative analysis like getting those 90 percent signals. Very often when we’re dealing with quantitative findings we’re looking at correlation data. Finding causation requires more sophistication but it is often qualitative findings that tip the balance. Interestingly, you can develop quantitative findings over qualitative findings but it takes a little more work. You need to ask questions differently and you might need to score the answers to get a quantifiable result.
Finding causation starts with asking open-ended questions. In my book, Solve for the Customer, I use the example of creating a new candy bar. The quantitative approach might ask about preferences like do you like coconut, prefer milk chocolate or dark, peanuts, almonds, pistachios, nougat—the possibilities are almost limitless. At the end of your research you might have a very detailed understanding of how much your target audience likes various components of a candy bar but you wouldn’t be any closer to making something that would sell.
The qualitative approach is less sexy in many minds because it implies that you won’t get enough information to work with, but consider this. In designing a candy bar, it would benefit you a lot if you also asked open-ended questions about what people like most about them or their favorite memories involving candy bars, or how they fit into a person’s day. Those questions are almost limitless too and the answers would surprise you and possibly tell you a lot about unmet needs in a crowded market.
If you don’t believe that’s useful, consider the story of Howard Moskowitz. Back in the day there were two competing makers of jarred spaghetti sauce Ragu and Prego. Prego was the perennial number 2 in the market and wanted to take the lead and they hired market researcher Moskowitz to figure out how. At the time there were also only two kinds of sauce on the market, plain and spicy. That’s it, just two. Moskowitz hired chefs to make what was ultimately 45 kinds of sauce, many with chunks of things in them like tomato, meat, and other veggies.
Moskowitz discovered that about one third of the American public wanted chunky sauce but incredibly, there was none on the market. Previous research was concentrated on getting quantitative answers to questions about existing choices, which can be boiled down to how do you like our sauce? There were no open-ended questions about what caused people to like spaghetti or Italian food. The Moskowitz taste tests provided the open-ended questioning leading to discovery of a new market that’s been worth billions ever since.
My point in all this is that you need both quantitative and qualitative information to arrive at correlation and causation if you hope to understand customers. If you’ve embarked on an analytics journey that’s great but keep looking and formulating your strategy. Buying a single product is definitely not the end of the journey but a beginning. If you’re a vendor, don’t make the mistake of thinking that your single product is the final answer to market need. It’s a stepping stone and you need to position yourself accordingly.
Where Self-Service Ends
Self-service is generally considered a good thing, especially in CRM where there has been significant investment in empowering people to take on more responsibility for provisioning service or making purchases. But it’s not all great and there is some interesting blowback that is causing vendors to reconsider how their offerings work.
In sales, self-service has resulted in truncated sales cycles and fewer invitations to the dance—you know this problem. Customers do their own research and engage a company and its representatives later when they are looking for pricing for their final decisions. Once vendors could control the information flow and customers would need to engage just to get a basic fact, that’s not true today.
We all know this happened as an inevitable consequence of the Internet and now vendors are struggling to find ways to engage customers earlier in their processes so that they aren’t locked out of deals before they can even find out about them. Also, nobody wants to be engaged at the last minute when all they can do is price, propose, and pray. We’re definitely dealing with unforeseen consequences.
Interestingly, and perhaps ironically, self-service has an equal and opposite effect in customer service. Customers are very happy not to wait in phone queues to ask simple questions and as a result, social media, FAQs, knowledge bases, and communities are supplying solutions for many of the issues that once consumed service reps’ time—another benefit of the Internet. This also means fewer touches, fewer chances to make impressions of try for the additional sale.
This doesn’t mean service is dead. In fact, with the easy questions handled by other means, more or less, the volume of issues might go down but their complexity has risen. As a result, engaging with contact center folk might take longer because even these veterans no longer have answers on the tips of their tongues and they have to do research. I don’t think this is as simple as a training issue. To be a service rep today means juggling more information, harder issues, and more customers.
This argues for a paradigm shift in sales and service, which is being driven by analytics and journey mapping. Previous approaches to sales as well as marketing have been reactive—the customer asks and the vendor responds. That makes a lot of sense because it is the nature of a conversation. But I’d suggest that conversations are not what customers seek. What they want is a relationship based on authentic engagement that is there when you want it but then vanishes.
Certainly, as customers, we seek and expect professionalism in all dimensions from the speed of dealing with us, to the correctness of whatever is provided as a solution to our needs. Too often though, we get wound around the axel of personalization because we erroneously believe we need to develop a personal relationship with customers but I think that’s a misunderstanding of the R in CRM. There are lots of ways to relate and most of them don’t become personal.
Let me suggest that the real goal should be the authenticity that’s born of professionalism and steeped in the understanding that as customers we’re busy and we want to get our issues dealt with so that we can move on to the next thing on our daily bucket lists.
That’s where analytics, especially machine learning, and journey mapping come in and while we’re at it this is the nub of what I am calling Customer Science. Machine learning can analyze your customer data and tell you where the clusters are, where the largest proportion of issues are and what the successful activities will be. With a product, you might discover through machine learning that many people get stuck at the same place or that they have a specific difficulty at the same place in a business process.
Armed with this knowledge, you can become proactive by building journey maps of your processes and thus prepare for the inevitable. Should you also use this knowledge to fix or improve a broken product or process? Absolutely. But that’s another department. In service and sales you have to deal with today’s reality today. So building out your repertoire of solutions for known moments of truth is a great way to build authenticity. It ensures your precision and professionalism and gets your customers back to their lives with minimal fuss.
So what’s better authenticity or personalization? Why must we think we need to choose? A warm greeting is always welcome and demonstrating empathy for a customer issue is not only good business, it is humane. It just shouldn’t be the only trick in our kits because no amount of being nice substitutes for a solution.
Salesforce Announcement Promises Better Marketing Processes
If you ever wanted to give yourself a nice, easy job you probably would not pick on-line retail marketing—the pseudo-math alone will kill you. Consider the algorithms that keep the balls in the air. You need to track and predict customer behaviors of all sorts like what was previously bought or even looked at, who the customer is demographically, as well as the vagaries of your own products, promotions, locations, people and who knows what else.
Ironically, these are the kinds of things that human beings can do rather well. I once went into a Nordstrom store to buy a shirt because a business trip went long. The woman who greeted me in the men’s department sized me up immediately and directed me to the color I wanted and my size all in about 10 seconds. I was amazed.
But people are finite resources, don’t scale well, and they are relatively expensive. My sales associate had a lot of colleagues and they all had the advantage of seeing customers to size them up. Today’s retail environment is not often that simple and we find ourselves dealing with thousands of customers at once who are often online and not subject to visual inspection. Consequently, we need software that not only does the sizing up, but that also figures out what we want or need, sometimes before we do. That’s the goal that modern retailer software vendors have set for themselves and something that Salesforce this week signed up for when it introduced Salesforce Marketing Cloud Predictive Decisions.
Like other products in this niche, Predictive Decisions analyzes customer engagement and proactively delivers recommended content, products, or offers, to create personalized customer journeys across channels at massive scale. It is hard to analyze this offering and to say whether it is better than a competing brand or not but that’s not the point.
Predictive Decisions is a great example of how companies can take the next steps in solving for the customer. In my recent book, I make the observation that empowered people + adequate computing + well-tuned processes are the secret to future business success and Predictive Decisions is a great example of this “rule” in action.
This is really the old People, Process, and Technology mantra turned on its head to great effect and if you break down my little formula you can reach a startling conclusion. Consider this: people and technology are in a dead heat today. Organizations understand that they can’t simply recruit and hold onto only the top talent and therefore they have to succeed with rank and file employees—that has always been true.
Likewise, technology per se (let’s say hardware to be specific) is not the game changer it once was because great, fast computing is now ubiquitous and cheap. So it’s really hard to steal a march on your competition to gain market advantage through hardware.
But in process we have access to an almost infinitely variable set of competitive advantages if we can adapt and leverage them. This is what Predictive Decisions promises to do in my view. With analytics and algorithms running at the speed and scale of technology rather than that of a person’s brain, a vendor can process huge numbers of variables and synthesize solutions that result in content, products or offers that are relevant to each customer.
But, don’t be confused, this isn’t about the offer or content—this amounts to building custom processes for each customer on the fly at the exact moment the process is needed. I know lots of vendors, and maybe even Salesforce, will disagree with me but the most important output of products like Predictive Decisions is not the recommendation whatever form it takes. What’s most important is that the system, by generating a unique process for each customer, remains authentic to the situation.
I use authentic when others might be comfortable with personal or similar words. But there is no such thing as personal anything when dealing with a set of algorithms; if the algorithms are good, they generate a facsimile of reality and that’s authenticity. Truth be told, most customers don’t want personal relationships with every vendor and attempting the personal might even hinder what ought to be a straightforward and professional interaction. That’s why I use authentic.
When my shopping experience is over, I am not having a beer with the associate or the computer. My highest goal is to interact with someone or something that gets me and therefore enables my success and that’s all about process. So congrats to Salesforce on yet another product release but this one isn’t about technology, it’s all about process and Salesforce customers should be glad about that.
Data and Its Derivatives
I took a trip in the Wayback machine last week when I attended DataWeek at the invitation of Dun and Bradstreet. As you might recall D&B is a data company collecting copious amounts of the stuff about individuals and companies and selling it for use in filling out profiles for business purposes — an over simplification but it will suffice. That data helps companies evaluate business risk such as when to extend credit to an unknown entity and companies happily subscribe to the service to help with decision-making.
D&B has been doing this kind of business for decades and my Wayback machine experience had little to do with them directly. It was more about all the other companies that were at the show. They were embryonic for the most part focused on Big Data and analytics and the experience was much like a few decades ago when the tech industry was beginning and every company had data or technology in its name.
Then and now the discussion was about data though in the earlier incarnation the conversation focused on just having data in digital form. Today it’s all about storing the stuff and extracting meaning from it in the form of information that businesses can use to create the knowledge they need to make decisions.
DataWorld is an interesting venue for a certain kind of technologist interested in data analysis but I’ve seen this movie. There was very little discussion of business or of the cultivation of data beyond analyzing it for some not-well-defined downstream uses. So my Wayback experience was all about what typically happens in early markets. Vendors talk about their technologies hoping that early adopter buyers will find merit in the goods and be willing to attempt doing something useful with them.
From my perspective the proceedings seemed to mirror what I’ve been discerning from data I’ve collected on the business uses of data, which is to say that it really is early days for business too. While many companies are adopting analytics and approaches to Big Data to help them make better sense of their businesses, many are still using the new technology to answer old questions rather than explore new ones.
For instance, one of my big issues lately has come down to time-stamping data in sales and marketing funnels. You might think this idea is old enough to have universal acceptance but it doesn’t. Time stamps on major milestones in either sales or marketing can augment the data we collect routinely from all sources and lead to a great deal of new information.
For example, a simple subtraction operation on two time stamps can tell you how long it takes to do something such as move from one stage to another in a process and it therefore turns static data about a process into a real representation of the process itself. With that you can begin to determine average speed through a stage or the entire pipeline and deduce — for predictive purposes — what a “good” opportunity looks like from your data. Prediction is one of the grail quests of modern analytics and not time stamping shows how early we are in getting it all right.
That’s really the utility of big data in a nutshell and the opportunity of predictive analytics; a simple tweak that provides significant business value. But we’re not there yet, at least not all of us and that brings be back to the Wayback machine. DataWeek convinced me of how early we are in the data revolution and how much we need to do to truly enable our front office business processes to run on information.
While we’re at it, perhaps this is a good time to seriously consider what we call the era that is being shaped by big data. While it’s true that data is at the core, the business need, and something that wasn’t much in evidence at DataWeek, is for knowledge. Knowledge, and not raw data, is what people use to make business decisions and it is delivered whenever information enters a brain to be manipulated and evaluated. Naming a show, or an era for that matter, after its most salient feature rather than after some benefit is not the best way to enlist powerful new followers and the big data movement could use a few more big thinkers.
While big data and data more generally is all the buzz these days, it is not helping people envision a solution that makes life better or more profitable. Perhaps this is a partial explanation of the hype-cycle, the early phase of a market when the promise of a new technology begins to greatly outstrip its ability to deliver tangible results. We’ll get through that phase as we usually do and on the other side things like DataWeek will get new names as we figure out how to make money through simple applications of big concepts — like time stamping.
Which Tools Do You Use?
One of the subtexts to the marketing automation explosion is analytics. Having a CRM system might make you wonder why marketing automation is needed at all but the reasons boil down to analysis and improved data collection.
Let me share some information with you from a new marketing study I did this summer. First the data — we asked marketers if they used CRM, marketing automation and business intelligence to process marketing data. The big dog was CRM with a 54 percent share. Next came marketing automation with a full third, and last was business intelligence with 22 percent. So far, so good.
I also asked what kind of data marketers collected and got some typical responses. From that data we get measures like cost per lead, cost per program, cost per revenue dollar, total leads and similar things — heavy on controlling costs. But only single digit responses came in for time stamping marketing events, measuring deal velocity and average lead time in a marketing stage. To me these are all critical measures that still too few people are using and it’s sad because these things separate monitoring marketing performance from managing it.
When you put the two things together — whether or not they’re using analytics and what kind of data companies are collecting, and thus analyzing and reporting on, you see there’s some work to do. I’d say from this data and some other stuff I am tracking, that we are squarely at the monitoring and not the management stage of the marketing revolution. In other words it’s still early days. That’s not to say that there are no companies out there doing the right thing the right way, just not enough to move the needle much.
What would it look like if the needle was moving? Good question. I think we’d be moving away from monitoring marketing as a cost and more companies would be managing it as a strategic weapon. When you can collect the data we’re collecting — which is pretty good, though not perfect, we still need more of a sense of time — and use analytics rather than simple report writers, will be able to cross tabulate seemingly unrelated data to derive new insights.
For example, suppose you were half way through the reporting period and it was clear you were going to miss your number. What would you do to rectify the situation? Obviously, you’d have to put more deals in the pipeline but, and this is critical, do you know how to generate leads with relatively short fuses so that the marketing spend will have an influence on the current quarter? You can depend on sales to beat the bushes to see what happens and you can always try to find some low hanging fruit in the customer base for upsells or cross sells. But that’s rather random and it has a 50/50 chance of working. In the end everyone would look heroic for trying and some heads might roll but that isn’t the same as making the number.
That’s a tough assignment but one that analytics is suited to providing an answer for. If you time stamp marketing events, chances are good that you can figure this out without breaking a sweat because you could figure out close time by program. If you had two marketing programs, one with a close time for leads that’s less than the 45 days or so that you have or one with a 90 day rate it’s a no brainer which one you’d use but so many marketing organizations don’t yet slice and dice their data to provide these results.
So while it’s good to be able to tell the CFO what you spent and the VP of sales the number of marketing qualified leads you generated and how many of them were accepted by sales, none of that really talks the talk of the boardroom which is much more along the lines of winning and not simply doing your job.
Some of my other data also shows that sales is still not providing enough feedback to marketing on the leads it develops and that’s too bad. But I suspect it’s because the two organizations — in too many instances — have not come to agreement on what a lead is or what an opportunity is and why it’s important to give feedback. It also suggests that there isn’t enough agreement on the marketing-sales process and handoff.
What happens when a sales person rejects an MQL? It should go back to nurture in many cases, but too often marketing doesn’t have visibility into the sales pipe and rejected MQLs just evaporate rather than going back into the hopper. So to net it out, we’re making progress, there’s more to do, and these are classic signs, to me, of an early market in marketing. And the tools you use determine the kind of results you can imagine. Funny how that works.