Tag Archives: big data

Digital Analytics – From Back Office to Front Page

21 Mar

Analytics have exploded into prominence in the past 15 months. What was once a mysterious statistical discipline understood by few has been elevated as the enabling technology that allows companies to unlock the potential of Big Data. Big Data was everywhereBlackboard in 2012. There was a track devoted to it at the World Economic Forum in January, 2012. In October, the Harvard Business Review had a cover section on Big Data which characterized analytics as sexy and dubbed its leading practitioners Data Scientists. And at Ketchum, we made analytics training mandatory for ALL employees in 2012, a first for our industry.

The digitization of all forms of analog data is at the heart of the Big Data explosion.  From our click behavior online to purchases we make with a loyalty card to places we go in our vehicles, everything is captured as digital data potentially available for analysis. And with the accelerating use of cameras and sensors, the volume of data promises to keep rising for years to come.

For all the talk about Big Data, no one really wants Big Data. They want the insights hidden within the data that only digital analytics can unlock. Probably the hottest area within digital analytics is predictive analytics. Predictive analytics essentially predict how consumers will behave given certain conditions, assumptions and stimuli. This has powerful and tangible benefits to marketing. An Aberdeen Group study published in December 2011 found marketing organizations that applied data mining and statistical modeling to optimize marketing efforts saw as much as a 2X lift from marketing campaigns, a 76% higher click-through-rate and a 73% higher sales lift.

Marketing organizations are seeing the tremendous power and potential of predictive analytics across a broad spectrum of marketing activities. For example:

  • Proactively optimizing marketing campaigns to improve engagement and conversions
  • Identifying customers most likely to switch companies and targeting offers to them designed to keep them as customers
  • Delivering customized offers designed to appeal to a prospect’s specific interests or life situation
  • Predicting which ‘first visit’ customers are most likely to return or not return, and sending offers targeting the ‘not likely to return’ group while avoiding the costs of making offers to those likely to return anyway.

In 2013, we have seen analytics continue to be a mainstream news subject. Analytics is only in the early migration phase from early adopters to mainstream use. Expect this trend to continue and accelerate in the coming years as more marketers discover analytics are a clear path to improving marketing effectiveness, efficiency, and ultimately, the bottom line.

Three Keys to Insight Discovery in Social Listening

13 Dec

True social insights, as opposed to social findings or social observations, have the potential to inform, shape or drive marketing and even business strategy decisions, not just social strategy decisions. Discovering that tweeting with a link on Tuesday between 10:00 – 11:00 AM drives higher levels of engagement is a social finding, not an insight.

Social media is a microcosm of the larger Big Data problem/opportunity – too much data, not enough insights. Or if you prefer, too much noise, not enough signal. If you want to improve your ability to discover insights, here are three simple approaches you can take to improve your insight hunting.

First, start all analysis with a hypothesis or series of questions the analysis is designed to answer. It is much easier to prove or disprove a hypothesis, or answer specific questions, than it is to “find out what people are saying about us in social media”. The more specific the request, the better the answer is going to be. The hypothesis may be one you develop based on preliminary analysis or it may come from the ‘customer’ for the insight. Here are two examples of hypothesis:

  • “Conversation about us in social media is quite negative. My boss believes ‘everyone’ is aligned against us. I disagree. My hypothesis is that there is a very vocal and active minority of consumers who are posting large volumes of negative content about us. And I believe this group is a small fraction of the total number of people who post. The majority of consumers are actually neutral toward us.”
  • “When we look at Twitter, Facebook and Blogs we see pretty low levels of conversation about our product and the medical condition it treats. We believe there is actually a fair amount of conversation, but the conversations are occurring in Forums which may not be crawled by most social listening tools.”

While the hypothesis is a great way to begin to focus on what is important in the data, a further focusing mechanism is the second insight discovery key – the concept of targeted listening. With targeted listening we are not trying to capture all conversations that mention the brand or product. That is a ‘boil the ocean’ approach. Instead, we listen for very specific types of conversations or conversations by very specific groups of individuals within social conversations. The trick is to have the discipline to only listen to your focus areas and not be tempted to boil the ocean in hopes of finding a few pearls. Here are three examples of targeted listening strategies:

  • An insurance company resists the temptation to try to capture ‘all’ mentions of the brand and decides to focus only on conversations where customers are thinking about non-renewal or switching companies.
  • A gaming company launches a new product and listens to understand what features are being discussed, what people like most/least about the new game and to gauge their specific reactions to the cover art.
  • A consumer products company listens only for consumers who are actively in the purchasing process for the type of products they offer.

The third key to discovering insights is to provide context for decision-making. Remember with insights we are trying to inform, shape and guide decision-making. Context is incredibly important to making better decisions faster. Good social analysts understand how marketing and business work and how strategic alternatives might impact results. Understanding this helps you put your insights in the proper context for decision-making.

Here is an example of how context can lead to better decisions. Company X has a crisis. You are asked to do real-time listening of the crisis and help the PR team decide when and how to engage in the conversation. You come back the next day with a line chart showing a large spike in content mentioning the crisis – thousands of mentions. You know the sentiment in negative to neutral and on which channels the content appears. Unfortunately you have not given the people deciding Trend.BlogPost

whether or not to engage enough information to make a decision. What information would provide the necessary context for decision-making? What questions do we need to try to answer? Here are a few:

  • How much above ‘normal levels’ is the spike in content? (Normative data)
  • How does this event compare to that event we had last year? Or, how does the event compare to competitor X who had their own crisis last year? (Comparative data) Comparisons help decision makers determine ‘how bad is bad’.
  • How long do we anticipate seeing negative content at relatively high levels? (Comparative data) This might be the most important question to answer to provide context and guidance for the engagement decision. If we anticipate volume will drop back to normal in a reasonable period of time, then not engaging may be a viable and effective strategy depending on the brand involved and the nature of the crisis.
  • Which stakeholder groups are active in the conversations?  With robust social analysis we always want to look at both the post – what is being said, and the source – who is saying it. In a crisis, who is talking is particularly important.

Normative data, comparative data and examining both post and source data are all effective techniques to provide context for decision-making. InsightButton

The tough part about discovering insights is there are no shortcuts and it is a human activity. No social media analysis platform that I have found has an insight button. The key barrier is lack of people who understand how to search for and discover insights. Hope these tips make you a more effective explorer.

Happy Holidays!

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