Data alone doesn’t provide insights, data connections do

SUMMARY:

Barb Mosher Zinck issues a corrective: we don’t need data; we need connected data. She makes her case with two scenarios. One involves breaking down data silos for a “360” view of the customer. The other addresses the thorniest of marketing analytics problems – lead attribution. In each case, we have real world examples to consider.

data-insightWhether you are trying to figure out the most effective media mix model or wanting to understand how your prospects are interacting and engaging with you across the entire customer lifecycle, you need data. Actually, you need more than data; you needed connected data.

Data insights for the complete customer lifecycle

Data-driven marketing gets the biggest hype these days, but data is used throughout the organization for so much more. The first challenge is connecting the data silos that exist across departments and even within departments to achieve that often elusive 360 degree view of the customer.

Steve Earl, Senior Director of Product Marketing at Webtrends, walked me through some examples where connecting data across the organization supports smart customer experience strategies outside of the acquisition world.

Earl said that when you look at your data post-silos, you have the ability to look at individual customers in a much more nuanced way. To look across silos and gain actionable insights from the data requires a cross functional team that pulls from key customer touch points across the organization: marketing, sales, service, and support.

Financial services are one area Earl noted that is doing a lot more with data to improve customer touch points. He described one bank that is taking online behavior and connecting it with product holding data and demographic data. Together, this data is used for lead nurturing, acquisition and churn analysis.

Another example Earl provided was a large travel company that connected clickstream data with offline patterns to help tweak pricing. Loyalty card data is another type of data that when analyzed with product purchases and clickstream data can help an organization optimize its product offerings.

The point here is that data can help do more than identify the best prospects. It can help understand customer needs after the purchase so you can provide great services and support throughout the customer lifecycle.

I work for an enterprise search client who says that data is great, but it’s the connections between the data that provide the most insights.

Anda Gansca, CEO of Knotch, a real-time digital intelligence provider, agrees. She said, “Any real layer of value is levels beyond data alone.”

Connecting data for marketing attribution

Marketing attribution is another type of analytics that is important to get a handle on today. According to Rise Interactive, a digital marketing agency, specializing in media, analytics and customer experience, interest in it is heating up now, and we all know why. Multichannel marketing is the norm, not the exception, but that being said, not every channel is equal.

In discussion with Rise Interactive’s Matthew Zaute and Todd Martin, I learned there are two key stages a company moves through to get a handle on attribution. The first stage is figuring out if they are measuring the elements of attribution critical to their marketing strategy. So you need to understand your audience intimately: where they are in the consideration cycle, what messages are resonating at different stages in the customer journey on different devices.

The second stage focuses on measurement. How do all the different media exposures contribute to conversion? This requires integrating media components together on the backend to get that full picture. The full picture can be challenging in the B2B space because a majority of conversions happen offline.

What organizations really want to know, Martin said, is how can they understand the customer better and get smarter on how to spend their advertising dollars.

Most organizations that decide to look at sophisticated attribution modeling already have an understanding of where their shortcomings are. They understand that the sum of the parts is greater than the whole, but that idea isn’t exactly reconciling with what the CFO sees.

Martin said they worked with a company in the education space to understand where their effective marketing spend was across all their channels and where it wasn’t. With this information, they could reallocate spending to drive better ROI.

To figure that out, the entire digital marketing stack was integrated to help improve efficiency and drive incremental value. A big challenge the client had though was that a significant portion of their marketing budget was spent on offline events around the campus. So along with integrating ad serving platforms, search engine, and ESP, they also had to do a great deal of data normalization for offline events to bring them into the mix.

Doing all this work in-house doesn’t happen overnight. Rise Interactive works with clients to help them understand how the models are created and the insights they can provide. It’s important, Martin said, to pull out base-level brand equity assumptions that form the foundation of the model and then understand the interplay between different marketing channels. As customers move through their journey, these channels often cross thread, so while there is a big appetite to understand, it’s an aptitude that needs to grow over time.

The thing about attribution is that it’s disruptive – it changes the way you view your data. And it makes it all the more important to ensure not only the quality of the data but its governance.

Final thoughts

We all understand that capturing data is important and that we need to ensure that data is of good quality. And we understand that data is useless if we don’t analyze it and take action from the insights it provides.

But perhaps the biggest challenges lie in connecting all the different data together and looking at it as a whole. The insights you get from a single data set simply don’t give you a complete picture, and you might be making changes for the wrong reasons.

Connecting data is critical, but understanding those connections and what they mean is even more critical.

 

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