2 items tagged "predictive intelligence"

  • Using Business Analytics to improve your business

    business analytics new

    I have often loudly advocated that enterprise performance management and corporate performance management is the integration of dozens of methods, like strategy maps, key performance indicator (KPI) scorecards, customer profitability analysis, risk management and process improvement. 

    But I have insisted that each method requires imbedded analytics of all flavors, and especially predictive analytics is 

    needed. Predictive analytics anticipate the future with reduced uncertainty to enable being proactive with decisions and not being reactive after the fact, when it may be too late.

    A practical example is analytics imbedded in strategy maps, the visualization of an executive team’s causally linked strategic objectives. Statistical correlation analysis can be applied among influencing and influenced KPIs. Organizations struggle with identifying what KPIs are most relevant to measure and then determine what the best target is for that measure. 

    Software from business analytics vendors can now calculate the strength or weakness of causal relationships among the KPIs using correlation analysis and display them visually, such as with the thickness or colors of the connecting arrows in a strategy map. This can validate the quality of KPIs selected. It creates a scientific laboratory for strategy management.

     

    Using the example of professional baseball, an evolving application of business analytics relates to dynamic home stadium ticket prices to optimize revenues. The San Francisco Giants experiment with mathematical equations that weigh ticket sales data, weather forecasts, upcoming pitching matchups and other variables to help decide whether the team should incrementally raise or lower prices right up until game day. The revenue from a seat in a baseball stadium is immediately perishable after the game is played. So any extra available seat sold at any price directly drops to the bottom line as additional profit.

    Another baseball analytics example involves predicting player injuries, which are increasing at an alarming rate. Using an actuarial approach similar to the insurance industry, the Los Angeles Dodgers’ director of medical services and head athletic trainer, Stan Conte, has been refining a mathematical formula designed to help the Dodgers avoid players who spend their days in the training room and not on the ball field. A player on the injured reserve list is expensive in terms of the missed opportunity from their play and the extra cost to replace them. Conte has compiled 15 years of data plus medical records to test his hypothesis that predict the chances a player will be injured and why. 

    Greater statistical analysis is yet to come. The New York Times has reported on new technology that could shift previously hard-to-quantify baseball debates such as the rangiest shortstop or the quickest center fielder from the realm of argument to mathematical equations. A camera and associated software records the precise speed and location of the ball and every player on the field. It dynamically digitizes everything, allowing a treasure trove of new statistics to analyze. Which right fielders charge the ball quickest and then throw the ball the hardest and most accurately? Guesswork and opinion will give way to fact-based measures. 

    An obsession for baseball statistics

    Gerald W. Sculley was an economist most known for his article, “Pay for Performance in Major League Baseball,” which was published in The American Economic Review in December 1974. The article described a method of determining the contribution of individual players to the performance of their teams. He used statistical measures like slugging percentage for hitters and the strikeout-to-walk ratio for pitchers and devised a complex formula for determining team revenue that involved a team’s won-lost percentage and market characteristics of its home stadium, among other factors.

    The Society for American Baseball Research (www.sabr.org), of which I have been a member since the mid-1980s, includes arguably the most obsessive “sabermetrics” fanatics. As a result of hard efforts to reconstruct detailed box scores of every baseball game ever played, and load them into accessible databases, SABR members continue to examine daily every imaginable angle of the game. Bill James, one of SABR’s pioneers and author of The Bill James Baseball Abstract, first published in 1977, is revered as a top authority of baseball analytics.

    How does an organization create a culture of metrics and analytics? Since it is nearing baseball’s World Series time an example is the community of baseball, including its managers, team owners, scouts, players and fans. With better information and analysis of that information, baseball teams perform better – they win!

    Legendary baseball manager Connie Mack’s 3,776 career victories is one of the most unbreakable records in baseball. Mack won nine pennants and five World Series titles in a career that spanned the first half of the 20th century. One way he gained an advantage over his contemporary managers was by understanding which player skills and metrics most contributed to winning. He was way before his time in that he favored hitting power and on-base percentage players to those with a high batting average and speed – an idea that would later become the standard throughout the sport.

    The 2003 book about the business of baseball, Moneyball, describes the depth of analytics that general managers like Billy Beane of the Oakland Athletics apply to selecting the best players, plus batter and pitcher tactics based on the conditions of the team scores, inning, number of outs, and runners on base.

    More recently, the relatively young general manager of the Boston Red Sox, Theo Epstein (who is now with the Chicago Cubs), assured himself of legendary status for how he applied statistics to help overcome the Curse of the Bambino – supposedly originating when the team sold Babe Ruth in 1920 to the New York Yankees – to finally defeat their arch-rival Yankees in 2004 and win a World Series. It ended Boston’s 86-year drought – since 1918 – without a World Series title.

    Author: Gary Cokins

    Source: Information Management

  • What is Predictive Intelligence and how it’s set to change marketing in 2016

    Screen-Shot-2016-01-27-at-14.02.51Explaining how modelling of marketing outcomes can let you make smarter marketing decisions

    As 2016 gets under way we're seeing more discussion of the applications of Predictive Intelligence. It’s a nascent field, but one that is gaining popularity fast and for some very good reasons, which we will discuss in a lot more detail in this article. We’re going to start this article off by explaining what precisely Predictive Intelligence is, we’re then going to provide some hard stats on its impact in the marketing world so far and are going to finish off by explaining how we feel it’s set to shape marketing in 2016 and beyond.

    What Is Predictive Intelligence?

    Despite the buzz surrounding Predictive Intelligence, many still don’t know what it actually is, so here is our definition. Predictive Intelligence is often used interchangeably with terms like Predictive Recommendation, Predictive Marketing and Predictive Analytics. Although there are some minor differences between the terms, broadly speaking they all essentially mean the same thing.

    Our definition of predictive intelligence for marketing is:

    "Predictive Intelligence is the process of first collecting data on consumers and potential consumers’ behaviours/actions from a variety of sources and potentially combining with profile data about their characteristics.

    This data is then distilled and interpreted, often automatically, by sophisticated algorithms, from which a set of predictions are made, and based on these, rules are developed to deliver relevant communications and offers to consumers to persuade them to engage with a business to meet its goals".
    You can see that because of the three-step process of analysis, interpretation and implementing rules for automated communications, a single sentence definition is difficult to devise! But, we hope this shows the essence of Predictive Marketing.

    McKinsey view it as applying mathematical models to best predict the probability of an outcome. They cite customer relationship manager example using models to estimate the likelihood of a customer changing providers known as ‘churn’. Other examples uses sources including everything from CRM data, marketing data, and structure data such as click through rates or engagement levels.

    The relevant actions that are carried out based on this distilled and interpreted data are that of predicting and then executing the optimum marketing message (e.g. image based vs text heavy / formal vs informal) to specific customer’s/potential customers across the optimum marketing channel(s) (e.g. social media vs email), at the optimum time(s) (e.g. morning vs afternoon) in order achieve your company’s marketing goals. These goals being usually higher engagement and/or sales. In summary, you are communicating in a way that is simultaneously most relevant and preferred by the customers/potential customers and most likely to result in you achieving your marketing goal(s).

    Essentially, you set what the marketing goal is and the Predictive Intelligence algorithms will then make good use of the collected data to find the optimum way of achieving it. Predictive Intelligence, aims to deliver content based on customer needs essentially tailoring the experience for the person receiving the information. Predictive Intelligence, empowered by data, thus begins to usher in true personalised one to one marketing communication that is aligned with a company’s marketing goals.

    Some stats and examples showing the value of predictive intelligence

    While we’re sure all the above sounds great to you, understandably, there is nothing more convincing than some cold hard stats on how Predictive Intelligence is actually performing. So without further ado, check out the below.

    As mentioned in IdioPlatform.com, research firm Aberdeen Group conducted an in depth survey for their paper titled “Predictive Analytics In Financial Services” where they interviewed 123 financial services companies. They uncovered that the companies utilising Predictive Analytics typically managed to achieve an 11 per cent increase in the number of clients they secured in the previous 12 months. Further, they saw a 10 per cent increase in the number of new client opportunities that were identified in comparison to those that have not utilised Predictive Analytics. Pretty impressive.

    Additionally, a Forbes Insights survey of 306 execs from companies with $20 million or more in annual revenue found that of the companies that have been carrying out predictive marketing initiatives for at least 2 years, 86% of them have “increased return on investment as a result of their predictive marketing”.

    Finally, in a study by toneapi.com it was found that by understanding the correlation between emotions expressed in the communications and the subsequent click-through rate. Based on this insight, toneapi.com was able to use the understanding of the model to predict how the airline could increase its click-through rates by appealing to certain emotions that would generate more interest from its customers.

    In summary, Predictive Intelligence drives marked improvements across marketing channels.

    The Emotional Connection

    Initially one of the big advantages of algorithmic Predictive Intelligence was the removal of emotion from the equation; human feelings and mood played little part in the decision as the computers choose the best course of action based on hard data. Now, as processing speeds and storage increase and the analysis of unstructured data improves we are seeing companies move into a more fluid form of Predictive Intelligence based around sentiment and emotion. The driver for this is that emotional analysis of text can help drive understanding of the dynamics that are causing key effects. These insights can be then used to optimise the content to match these emotions creating a more iterative and action orientated approach to marketing campaigns.

    These companies look at the emotions which motivate behaviour and utilize technology to predict and improve results. Toneapi analyses freeform text content (such as emails, press releases and brand copy) for emotional impact and then offers up suggestions for improvements. Likewise Motista studies have shown that “emotion is the most predictive driver of customer behavior”, they bring together big data and social science to increase profitability.

    Looking To 2016 And Beyond

    Up until now Predictive Intelligence has seen most action in the B2B world. B2B companies have been using it to crunch colossal amounts of data on customer/potential customer behaviours from a variety of sources. They have then been using it to automatically draw insights from this data based on a set of signals in order to score their leads, identify the most attractive leads earlier on and uncover new high value leads previously unseen. Crucially, Predictive Intelligence has then allowed the B2B companies to tailor the marketing approach and messaging depending on the customer/potential customer’s actions/behaviours (signals) across the different areas where the data has been collected.

    We believe that in 2016 we’re going to see more of the above, and the process is going to become even more refined and sophisticated within the B2B sector. Also, we feel 2016 is the year we see Predictive Intelligence move more and more in the B2C world too, especially now that its frequent use across industry sectors in B2B has proven its effectiveness and given the approach some real credibility. And, we see more interest in Predictive Intelligence around emotion analytics, free-form text, unstructured data and behavioural social sciences.

    Additionally, now, unlike even a couple of years ago, there are quite a few smaller Predictive Intelligence companies on the market in addition to the big names like IBM or Salesforce. Many of these companies are offering intuitive, easy to understand, well designed and well-priced cloud based Predictive Intelligence software packages. This lowers the barrier to entry greatly for Small-to-Medium businesses (SMB’s). It allows them to dip their toes into the world of Predictive Intelligence and test the waters, with little risk or friction, or if they wish, jump straight into the deep end of Predictive Intelligence and reap the rewards.

    Thus a whole new world has opened up to the SMB. A world that not too long ago was reserved mostly for the large corporations that could afford the expensive Predictive Analytics software (which was the only real choice) or that had the budgets big enough to hire data scientists to crunch data and draw insights from it from which to base their predictions.

    Conclusion

    We hope this article has gone some way in demystifying the phrase “Predictive Intelligence”. Further, we hope we have communicated the immense benefits to be reaped if Predictive Intelligence is executed properly. Benefits in the form of higher engagement, higher click through rates, higher conversion rates and emotional impact. Predictive Intelligence has already seen some real traction in the B2B world, and we believe 2016 will mark the year that the B2C companies and SMB’s in general adopt Predictive Intelligence in a meaningful way. Some dipping their toes in and giving it a try and others jumping straight into the deep end and really embracing it.

    Source: Smart Insights

EasyTagCloud v2.8