Causata at London Big Data week

Last week was Big Data Week in London, with a packed week of excellent meetups on Big Data, Data Science, Data Visualisation, and the opportunities afforded by Big Data. We at Causata made the most of the week, attending lots of events and meeting some interesting people in the thriving London tech community.

I spoke at the Data Science meetup and talked about what it takes to determine cause and effect from data, and how I believe that the key is to perform experiments. One of the great strengths of Causata's real-time decisioning is that it allows statistically rigorous, controlled experimentation, allowing us to move beyond correlation to understand causation. Here are the slides I showed.
—Jason

The importance of getting data into production quickly

Flowing Data is a great website for data visualizations. I love the one showing the sequence of Walmart store openings over time or the perfect choice of bike for each San Francisco neighborhood.

However, as someone who not only enjoys looking at data but is also focused on actually doing something with it, I felt today's article really hit home.

For a few years the Netflix prize has brought the top data scientists on the planet into an annual open competition to set a new bar on best movie predictions. It's the modern day equivalent of the KDD Cup, which was where Causata's COO Paul Phillips cut his teeth. The article highlights that the algorithm from the latest winning team was deemed not practical enough to put into production. I'm actually not that surprised -- though to be fair I doubt the competition had any requirements for that.

Nevertheless it does underscore a real gap I see in how people look at big data and making predictions with machine learning, especially when it comes to marketing or customer interactions. It's just not appreciated enough that the whole point of going through the effort to build a statistical model is to extract business value from it and that doing so quickly is essential. When I hear SAS analysts talk about how long it typically takes to put a model into production I'm always amazed. The months of recoding SAS code into SQL, the many compromises along the way, and the heavy validation effort in a database or data warehouse raise questions about whether it's the right approach.

This is a topic where I'm 100% sure we're way ahead at Causata. If you've built a statistical model in a tool like SAS or R it can be imported into Causata in seconds and any individual can then be scored in the next instant. Causata's real-time scoring also incorporates the latest data, including customer interactions in the last second. So if a new website visitor arrives via a high-value search term, a score will scream that they are a hot prospect. There are dozens of important use cases related to this capability. It's a huge deal. The philosophy for us is a bit like the agile software development process applied to data. You don't want a long period of time to pass before releasing a statistical model into production. Release early, release often.

Check out the full Netflix blog article that the Flowing Data piece refers to. The folks at Netflix explain how their business is evolving and this has changed the nature of the predictions and personalization they need to perform. This is just yet another reason why you need to get the data into production rapidly.

My prediction... in the next 12 months this agility topic is going to be top of mind for a lot more people. We see this growing awareness among the top marketing and analytics customers we're working with. How top of mind is it for you?

Keystone partnership and eMetrics SF

We announced a new partnership this past week with Keystone Solutions. They've got a great team with a similar digital marketing heritage to us. We're really excited about working more closely together. The full press release is here.

Stay tuned for many more partnership announcements to come.


Many of the Causata team will be at eMetrics this week in San Francisco. Paul Phillips will be speaking on Tuesday morning. Don't miss it!

Banking on data

Causata’s CEO Paul Phillips (left) moderated a panel at the BAI retail banking conference in Chicago last week.

Paul was joined (left to right) by:

Michael Wexler, Director of Digital Insights and Marketing Effectiveness at Citibank

Mike Olson, CEO at Cloudera

and Andrew Rosen, CMO at Bank of the West

The panel topic was “Winning and Losing with Customer Data in an Accelerating Digital World”. Michael and Andrew shared some illuminating stories about how important they see customer data and just how big an opportunity there is to impact their business and the customer experience. Paul and Mike gave their Silicon Valley thought leader take on where the technology is today and how companies should best take advantage of it. There was a clear consensus that the companies that embrace data will be the ones that win.

Determining the value of your data

John Lovett has a thoughtful post at Clickz.com summarizing many of the struggles enterprises are having with Big Data. One problem is enterprises often store as much data as possible, for as long as possible, fearing they’ll be discarding valuable information if they don’t. Enterprises struggle to understand which data-points are valuable and which ones aren’t. Lovett writes:

“Understanding what data matters to your business requires empathizing with business stakeholders, examining marketing programs, and getting to the mission-critical values of the organization. In my experience, I've found that simply asking business stakeholders what metrics or KPIs are most important to them is a futile endeavor.”

At Causata, we believe that the relative value of data should be determined by how it can be used to drive your business forward and help you achieve your goals. We begin by organizing all of the data around a customer. You should be able to see every interaction a customer has had with your company. Those interactions should be stored in time order, so you can see how events in the past, predict future behavior.

The next step is to understand your business goals. Most often these are centered on increasing profit either by selling more products and services or increasing customer satisfaction. Take reducing attrition. You’ll need to understand the behaviors that lead customers to terminate their relationship with your company. You may look at things like: How many products did a customer browse online in the past week? How many times has she called to register a complaint over the past month? When was the last time she visited a store location? Leverage as much of your data as possible, especially web and mobile data rich with customer intent. Now conduct an analysis to see how each of these behaviors is correlated with customers who end the relationship with your firm. In the process you’ve transformed your data into customer intelligence. This intelligence can be used to power campaigns to reach out to those customers most likely to attrite.

This is a tremendously valuable view of the data because it gives you the power to anticipate dissatisfied customers and reach out to them before they leave. In doing so, you’ll be able to significantly increase your business performance.

This can be extended to any business goal. In the end you’re left with thousands of data-points that express customer intent. This, in turn, can guide an assessment of the relative value of various data-points. More importantly, it can be used to help you really understand how customer behavior relates to your business performance.

A look at Causata

There's a good post about Causata by James Taylor on his blog Everything Decision Management.

Gareth

Management doesn’t value marketing

According to a study cited in this article over at Marketing Week, 73% of CEOs believe that marketers “lack business credibility because they fail to quantify the success of their campaigns”. It goes on further to state that: “marketers focus too much on the ‘arty and fluffy’ side of marketing and not enough on its business science”.

Putting a hard number on the value of a marketing campaign is an ongoing struggle for many marketers. It requires a complete view of the customer, something that marketers may not have access to. Imagine a marketing campaign for a bank that leverages banner advertising, call center and direct mail with the goal of increasing product holdings among existing customers. Management rightfully wants to understand the business value created through this campaign.

To begin to answer this question, you’ll need a complete view of the customer, both before and after the after the life of the campaign. This complete view includes all customer interactions along with all other customer attributes. What banners was the customer exposed to? How many did she click on? Was she targeted for with direct mail? How did she respond? What are her total account holdings before and after the campaign? And so on.

Without this view it’s almost impossible to understand the cause of changes in product holdings because you’ll only be analyzing part of the story. This leads to approximations that can be divorced from business reality. Marketers are often forced into approximations because some number is considered better than no number at all. But this can lead to a big problem: When marketers report numbers that fail to align with financial statements, distrust of marketing grows.

Developing a complete view of the customer is a necessary condition to truly understand the success or failure of marketing campaigns. Marketers are going to continue to have trouble understanding the value they’re creating without it. And without understanding the true value they’re creating, business credibility may continue to elude them.