Coping with Metrics Overload

Not a day goes by when we aren’t confronted with one of the latest buzzwords: big data, data analytics, data visualization or dashboard metrics. But is access to all of this data really living up to the hype?  Sometimes it seems that the cost of these data outweigh the benefits. Instead of providing useful insights, oftentimes the data contributes to information overload.

One reason for this is that the technology we’ve been developing in this space has focused on collecting, storing and processing more and more data. Not enough efforts have been directed toward developing reliable metrics and models that can consistently link the data to company performance.

Recently, The Walt Disney Co. started piloting wristbands that allow resort guests to unlock their hotel rooms, pay for meals, enter the theme parks and obtain passes to attractions and rides. You can imagine how much data this technology would generate. You can also imagine the potential insights you can extract from the data. But for those who have had the privilege of managing data analytics efforts, you can also imagine drowning in  pages of reports and metrics. How much money do guests spend on an 85-degree day versus a 60-degree day?  How frequently do guests return to their rooms during their stay and when?   Is gift shop purchasing correlated with what rides and attractions visitors get passes for?

Disney has the sophistication and resources to effectively use  this data for their marketing and operations efforts, but we can’t all be Disney. So how do we handle all of the data and metrics generated from our technologies, ranging from sales metrics and social media metrics to market research and customer tracking data?

1.  Stay focused. The first step is to start with strategic goals, and structure the data collection and analysis around those goals.  This is common knowledge in offline marketing research but somehow it has been lost in the world of big data analytics, where analysts are seduced by interesting metrics and data visualization.  Instead of asking, “I wonder whether gift shop spending is correlated with passes for rides and attractions?” we should be asking, “Who are our heavy spenders and how can we identify them?” The first question results in an interesting correlation metric that may inform a strategic decision. The second question stimulates a suite of metrics that would clearly inform segmentation and targeting strategies.

2.  Measure what matters. Too often, we build metrics based on what is easy to measure instead of measuring what matters. Thus, once we identify our strategic goals and questions, we need to hone in on the metrics we would need to answer those questions. We want our strategic objectives to guide our analysis and data collection rather than letting the data structure define the metrics and analysis. When we let the data drive the process, we often end up with an abundance of metrics that we don’t really know what to do with.

3.  Establish a baseline and set benchmarks. While it might be great to know how much the average customer spent in the gift shop after riding Space Mountain, it’s not clear if that is good or bad, or if any of our marketing efforts around the Space Mountain experience worked. What we need are baselines and benchmarks to measure success. Before launching any new promotion, we need to establish a baseline to tell us what our performance metrics normally look like. We also want to set a target for the metric that will tell us if our promotion was successful, profitable and worth the investment.

4.  Monitor past success. In the world of metrics overload, it is important to monitor success by tracking the metrics linked to the organization’s strategic objectives and comparing them to baseline and benchmark metrics after the campaign. Better yet, smart organizations should maintain a library of baseline, benchmark and outcome metrics to improve service. This would allow the organization to identify elements of their past activities that worked and those that fell short, and learn how to optimally design their activities moving forward.

Posted in: Measure what Matters

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Social Media Meets CRM

When the lights went out at 8:38 PM in the Mercedes-Benz Superdome during Super Bowl XLVII, Oreo’s message that “you can still dunk in the dark” took advantage of the situation to promote the brand.  The short message received more than 500 million earned media impressions and garnered digital agency 360i numerous awards for social media digital marketing.

For all the hype around the notion of real-time marketing and the potential to leverage social media, you would think that it’s something new. In some sense, it is. Social media provides another platform that brands can leverage to reach consumers, as well as delve into their interests and personalities. But, the underlying marketing principles are the same. It’s about delivering the right message to the right consumer at the right time. From non-profits seeking donations and politicians seeking votes to telecommunication providers trying to cross-sell products and retain subscribers, customer-centric marketing focuses on identifying those individuals where marketing efforts are expected to have the biggest impact. That is, it’s about identifying the right consumer.

What about the message and time at which to reach this consumer? Here’s where marketers can take advantage of social media chatter. For example, travelers ranting online about their horrific holiday experiences provide an opportunity for brands to make inroads with their rivals’ customers who are already dissatisfied. Consumers soliciting advice from social networks prior to making a purchase are a ripe target for brands to reach out to, tailoring the message based on the information being sought by consumers. Brands monitoring social media conversations can not only engage with their own customers, but also take advantage of social media data to identify other consumers to whom they should reach out. Beyond finding the right consumers with whom to communicate, social media offers a potential avenue through which marketers can reach these consumers at opportune times using the message content that is likely to be the most effective.

This may provide some explanation for Apple’s recently announced acquisition of Topsy, a social media analytics company for which Apple paid in excess of $200+ million. One explanation that has been proposed is that the insights derived from analyzing social media conversations could be used to support Apple’s advertising platforms. Another rationale that has been offered is that mining the social media conversations would support the recommendation systems in Apple’s App Store and iTunes.

At their core, these are doing the same thing: aiding Apple in understanding what will appeal most to its customers at a particular point in time. If I’ve purchased a season pass to Scandal or albums by Justin Timberlake in the past, algorithms can be used to figure out what should be recommended based on the purchases made by others with similar tastes. Add in signals from social media conversations, which research has found to be a leading indicator of offline activities, and such recommendations can be made dynamically. This same line of thinking would hold for serving advertisements within mobile apps. Just as there is heterogeneity in terms of the topics that are of interest to different individuals, resulting in some advertisements being better suited for some individuals compared to others, there is variation in when advertisements will be more or less effective.

The data available on social media have ushered in a new wave of what’s possible for marketers. We still need to find the right customers. Once we’ve figured that out, the analysis of social media data can provide direction for fine-tuning message content based on the context in which an organization is operating. Marketing is about the same thing it’s always been about: right customer, right message, right time. We’re just in a better position to implement it now. 

Posted in: SMI and CRM

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Day 1

Whenever we talk to organizations about social media intelligence, analytics and metrics, it always starts with them asking a laundry list of questions.  Who’s engaging with us on social media?  How can we characterize sentiment? Is our social media strategy working?  How do we calculate ROI?  How many re-tweets and mentions is good?

This laundry list of questions is common in organizations that have just starting thinking about social media intelligence but have not yet figured out how to prioritize, organize or interpret the data from their social media monitoring efforts. They are being overwhelmed by the sheer volume of data and metrics these monitoring efforts produce.

What we tell organizations is to stop measuring what’s easy to measure and start thinking about what’s important to measure.  What is your organization’s strategy and what are your objectives?  With strategies and objectives as the starting point, look for the appropriate metrics and analyses to help answer the questions, and don’t let the data and metrics drive your questions.

Posted in: SMI and Strategy

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