May 2013 Archives

When people ask me what I do, I usually say something about exploring the edges of the market for intelligence and analytics capabilities, starting with social media data. I also like to connect threads from separate topics and look at things from unusual perspectives. With that as a warning of sorts, let's pull some threads from new methods, old metrics, and emerging science to see what they do together. It may sound like so much theory so far, but this is all about practical analytics for management.

Thread one: A new view of social media in a customer journey framework
It started with a briefing from SDL on their new Customer Commitment Framework (CCF). I'm always interested to see people do something different with social media data, and I give bonus points for tools that provide quick and clear access to useful information.

SDL's approach is to monitor business performance at key points of customer journeys by analyzing what they have to say in social media. They want to know what people are thinking as they progress toward a decision, whether that decision is about buying a product, telling others, or becoming an advocate for the product. CCF's analysis is always presented in the context of a customer journey, so—in theory, at least—its numbers provide a drill-down into the performance of different parts of a company's marketing and operational performance as experienced by customers.

I haven't tried CCF and its dashboard component yet, but if it works as promised, its alignment to identifiable business levers could make it a valuable analytical tool.

Thread two: Exploring possible futures with agent-based modeling
Call it simulation to avoid scaring people off, but the complexity science tool of agent-based modeling has come to market. When I saw that Icosystem had spun up a company, Concentric, to offer ABM tools for marketers, I knew I needed to learn about it.

Concentric's book, How Customers Behave, was a good start, but some of my earlier reading and the Santa Fe Institute's MOOC on complexity made the background sections somewhat redundant. One key takeaway I've found is that, despite the complexity label, this stuff isn't too complicated to understand.

Where SDL looks for signals about what has happened, Concentric starts by building models of customer journeys, playing out the decisions faced by individuals in the market ("agents"). Once the model can "predict" the past, it's ready for use in simulating the effects of different strategies and tactics. Depending on your needs, their software can incorporate social media and other online data sources, or it can look broadly across media types and operational data.

Is this what you expected?
If you put threads one and two together, you get simulations to explore possible outcomes of different strategies, and measurement of customer opinion at critical points to indicate actual performance. One looks forward to explore what may happen, and one looks at the recent past to understand what has happened.

It seems like a powerful combination to me, but let's add one more thread. What about hard data?

Thread three: Marketing analytics and the view of the process
I once worked on a project for one of the big phone companies that was concerned about customer churn in their high-speed Internet business. They were adding new customers as fast as they could, and they wanted to avoid losing existing customers. Our analysis rested on the insight that sometimes you lose the sale even when the customer wants to buy. Before it was trendy, we looked at the post-purchase customer journey and found some measurable issues.

I usually see customer journey models that assume that customer attempts to purchase virtually always succeed. If you're selling online or in retail, that's probably close to true. But do you know that it's true, or do you assume it?

For the phone companies, DSL service circa 2002 was constrained by geographic footprint, technical limitations, compatibility issues, and customer ability. Each of these added another step in the journey and another opportunity to lose the sale. Given the operational metrics they already had—order attempts, accepted orders, activations, etc.—you could track post-order performance as a series of multipliers between zero and one. A simple step-down chart would show you where you were losing customers, so you would know where to invest to improve the process.

For a subscription-based business, recurring revenue is everything, so you need to pay attention to customer retention and anything that drives them away. This is already a long post, so let me point to a post from Keith Schacht about customer acquisition and retention, and a VOZIQ post on customer issues in telecom. The point is, your customer's experience may not end with the purchase, and your measurement of the experience shouldn't, either.

When you combine the effects of attracting new customers and losing existing customers, you end up with a survival analysis, which brings us back to complexity and the potential of agent-based modeling. The fact is, a given rate of customer additions and losses combine to set a ceiling on your possible customer base, so it's crucial to understand where and why you lose them.

Really, it all comes together
Pull these three threads together, and we start to see the potential of forcing analytics out of their comfortable silos. Agent-based models offer a tool for understanding how things might (not will) play out under different scenarios, including the possible outcomes of different marketing strategies. Analytics based on similar models of customer journeys provide a view of the recent past to test expectations and continually reevaluate the models. Combining social media data with customer data and operational metrics allows us to see a more complete picture: Where hard data is unavailable, social media indicators can fill the gaps. Where hard data is available, it provides a test of the social media indicators.

Pull the threads together, and you get a view that combines future and past; what people say and what they do; what happened and why. Sounds pretty useful to me.

Another sketch from the whiteboard

A couple of years ago, a suggestion that I develop a maturity model for social media analysis led to a different kind of model. My approach to this space has always been to explore its edges, looking for what might be next. One effect I've noticed is that change circulates through the ecosystem of companies, their customers, and their suppliers. Where change keeps coming, everyone's learning together.

A linear maturity model defines development stages toward a known destination, but in a system where everyone is learning, the destination is still unknown. We react to others, and others react to us. Change reverberates through the system, and we don't yet know what maturity looks like.

What this means in social media analysis
If social media analysis were good for one thing, we could have a simple maturity model. The products would progress toward a theoretical ideal, and clients would mature toward efficient, effective business practices. But the technology stack is built on areas of active research, new platforms are driving new consumer behaviors, more business functions are showing interest in how to use social media to do their jobs, and vendors are trying new ways to distinguish themselves.

Virtually every piece of the puzzle is moving.

Let's go to the whiteboard to see if we can visualize it.

There's a lot going on here, and this is the oversimplified version. Here's the basic dynamic: on the right, new capabilities become practices; on the left, new expectations become requirements. In the overall system, we expect more from our suppliers as we adapt to new capabilities and adopt new practices.

Think about what is being learned in each loop.

• Tool vendors combine their own R&D with new capabilities from research labs and partner companies to expand their products' capabilities, enabling new tactics for their clients, who provide feedback and new requirements based on real-world use of the products.

• Consumer-facing companies experiment with new tools and capabilities, and they learn from both operational results and customer reactions.

• Customers react to companies' online tactics, adjusting their behavior to maximize their own benefit. When they find a practice they like, they may expect other companies to mimic it.

The catch is that this is all happening at the same time, and the companies, at least, are trying to predict how their customers will want next.

Who's learning fastest?
We know of some unintended lessons, such as teaching customers to complain publicly for a quicker response, and redefining like. But where do we look if we want to get ahead of the market? Try these key areas:

• Outside innovation - New research and inventions may provide answers to questions you've wanted to answer.

• Product capabilities - What's possible keeps changing, but don't look only at existing suppliers. Look at adjacent markets for capabilities worth adapting to new applications.

• Client requirements - It's always worthwhile to pay attention to what companies say they'll pay for.

• Client capabilities - Watch what companies are actually using, too.

• Competitor actions - Watch early adopters for practices that may become standard. Is there a better way to do it?

• Customer expectations - How are people reacting to new business practices? What issues are being raised? What new expectations?

Like any model, this one raises more questions than it answers. That's the point. What will it help you discover?

More ideas from the whiteboard:

• Computer attention
  
• Omniscience

I'm sharing some of the frameworks that have been hiding on my whiteboard. Want to apply them in your business? Email me.