Customer Experience Suffers As Organizations Make Inconsistent Use of Analytics, Accenture Research Finds

Intuition still reigns over fact-based decision making when it comes to customer engagement

When asked to evaluate how well they are using analytics, such as applying data, statistical and quantitative analysis, descriptive and predictive models to drive decisions and enhance customer experience, most organizations’ perceptions are very different from reality, according to new research from Accenture (NYSE: ACN).

Accenture’s Customer Analytics Survey of 800 directors and senior managers at blue chip organizations in Brazil, China, Germany, Italy, Japan, Spain, United Kingdom & Ireland and the US and Canada showed that more than half (55 percent) of respondents felt their methods for segmenting customers and providing relevant experiences are either “ideal” or “very good.” However, related consumer research tells a different story. Accenture’s Global Consumer Survey[i] found that only 21 percent of consumers believe the companies they do business with are good at providing a tailored, relevant experience. Further, it showed that two out of three consumers changed service providers in the past year. With an average cross-industry churn (or defection) rate of 64 percent, organizations that hope to grow must provide more relevant and satisfactory customer experiences.

Most Organizations Don’t Know What They Don’t Know

The Customer Analytics research revealed that more than half of the organizations surveyed do not take advantage of analytics to help them target, service or interact with customers. This suggests that many organizations may be unaware of what is really important to customers and lack the capability to measure their performance in engaging with them.

When making decisions about what customers want, many organizations are just as likely to rely on personal experience (which 23 percent of respondents described as “very important”) as analysis of data and facts (which 22 percent called “very important.”)

“Ten years ago, organizations could get away with relying on intuition to make decisions about how to engage with customers but today’s world is wired and interconnected in such a way that customer expectations and influence are greater than ever before,” said Julio Hernandez, global lead for customer analytics at Accenture. “Descriptive and predictive analytics enable organizations to draw fact based conclusions about what customers are actually doing and what they are most likely to do and need. Whether in product features, delivery or price, organizations are leaving money on the table by not applying the power of analytics to these decisions,” added Hernandez.

Even Analytically-Minded Organizations Are Missing the Big Opportunities

Even amongst organizations actively using analytics in marketing, sales and service, most are not applying it broadly across the full spectrum of marketing and customer activities. This includes critical areas such as pricing (86 percent of respondents not using), product/service delivery (77 percent not using) and product development (59 percent not using). Given the impact that new product development and delivery have on an organization’s future growth potential, applying analytics to make better informed decisions in these areas is an opportunity for companies to gain a significant competitive advantage and maximize their market potential.

What is Holding Organizations Back?

The study revealed that less analytically mature firms are much more likely to perceive their data sources as accurate and reliable than organizations with more developed analytic capabilities. This may be because larger firms are conducting more regular and complex data manipulation and so are more aware of the shortcomings of their data and analytical capabilities.

Corporate culture also poses challenges. While almost 70 percent of respondents said their senior management was totally or highly committed to analytics and fact based decision making, corporate culture still presents a major barrier. Less mature firms are more likely to cite overall corporate culture as a major barrier (42 percent of respondents versus 33 percent of more analytically mature firms). The more analytically mature firms cite budget limitations (47 percent versus 29 percent), departmental culture (40 percent versus 29 percent of less analytically mature firms) and senior management support (45 percent versus 31 percent) as obstacles to better segmentation.

Failure to Recognize the Customer’s Perspective

The findings suggest that most organizations analyze and segment customers according to their value to the organization rather than according to their customers’ unique needs and preferences. For example, across organizations at each end of the analytics maturity spectrum, the most commonly used metrics to segment customers tend to be company focused, such as: profit per customer (cited by 41 per cent), lifetime value (27 per cent) and share of wallet (how much of a customer’s total spending occurs with the company) (24 per cent). Whereas indicators of customer requirements such as customer needs and behaviours, service levels received and psychographics are often used least.

“Regardless of how analytically mature they are, most firms largely focus on using analytics to better understand the customer’s value to the organization and much less on the organization’s value to the customer,” said Hernandez. “This leads to a customer experience that is over-indexed on meeting the needs of the organization versus the customer. Organizations will benefit from a more balanced approach to using analytics, one that takes into account what’s sustainable for the business as well as what’s relevant to the customer.”

About the Research
This research is based on 800 telephone interviews with customer facing directors or senior managers in major blue chip organizations with responsibility for, or knowledge of, the use and application of customer analytics in the company. The research was conducted in March 2011 across eight countries (Brazil, China, Germany, Italy, Japan, Spain, United Kingdom & Ireland and the US & Canada) with 100 interviews in each country.

For a copy of the findings please click here.

About Accenture
Accenture is a global management consulting, technology services and outsourcing company, with more than 215,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world’s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$21.6 billion for the fiscal year ended Aug. 31, 2010. Its home page is www.accenture.com.

[i] Accenture 2010 Global Consumer Survey , published February 8, 2011

Navigating Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Leveraging Contextual Network Information for Better Accuracy

Nick Lim is the CEO and Founder of Sonamine, LLC. Over the past 15 years, Nick has worked in analytic environments with large amounts of data. Leveraging his training at Harvard, he has led product and strategy teams at MicroStrategy, Enpocket and Nokia to build systems that predicted ad clicks and consumer behavior using terabytes of information.

Predictive Analytics: Benefits and Challenges of Using Graph Theoretic Methods Leveraging Contextual Network Information for Better Accuracy

The practice of predictive analytics has come a long way since the advent of operations research. Current predictive models routinely use more than 100 variables, most of them are characteristics of the object being analyzed, including age and gender for customer analysis, or color and text for advertisements. In this article, we will explore how graph methods can be used in predictive analytics.

Graph methods refer to techniques that analyze a network of objects, rather than pie charts and trend lines. Networks are made up of nodes and connections among the nodes. Other synonyms include vertices, edges, and arcs. Graph methods analyze the link structure, rate of diffusion and the clustering of nodes in a network. Many concepts in the popular press such as Google’s page rank, social network analysis, influencer marketing and driving directions all utilize graph theoretic methods.
Real World Use Cases
Why are these graph methods useful for predictive analytics? One reason is that additional contextual predictors can be derived using graph methods. Let’s start with a well known example: Google uses page rank as one of many predictors for the relevance of a web page. The link structure in the world-wide-web network provides valuable contextual information about which pages are deemed most relevant by the web page creators—this contextual link structure is then used to predict relevance for a user’s query.

Another example: If you are trying to predict the revenue contribution of each employee, in addition to the educational level, job responsibility and experiences, you might consider how well the employee relates to the customers or other managers. The contextual information added here is not something inherent about the employee. IBM conducted such a study with 2600 of their consultants, and created a model with revenue as the outcome target variable and three sets of predictor variables.1 In addition to the standard employee and job-related characteristics, two sets of contextual variables were added. One was related to the structure of the web of relationships, and the other was related to how each employee was related to managers and customers. It turned out that graph-derived contextual predictors were highly correlated with revenue.
Enriching Context Where It Seemingly Did Not Exist
One common question raised is how much of the contextual information is already captured in the demographic data. After all, there is a common understanding that birds of the same feather flock together. To tackle this question, a group of researchers at MIT studied how the Yahoo instant messenger network affected the adoption of the Yahoo Go product.2 They discovered that only 50% of the contextual network effect could be explained by the common underlying demographic variables.

This finding has big implications for predictive analytics in data poor environments. A data poor environment would benefit greatly from using contextual predictors that you already have. For example, if you are trying to predict churn in a prepaid mobile customer base where there is little to no demographic information, using the social network contextual information can greatly improve the prediction accuracy and lower the false positive rate.

Another type of contextual network information that can be included into predictive analytics is the underlying form of the social community that one belongs to. In the study with Yahoo data above that was trying to predict product adoption, they found that the chances of you adopting a product rises if more people in your social community have already adopted the product.

Besides social contextual network data that can be added to predictive functions, you can also deduce contextual information. For example, in trying to predict fraud from a set of credit applications, we can construct a network of information attributes. Using the basic idea of that people with bad credit tended to relate to other similar people, some researchers generated the graph-based hub and authority variables from such a network and added them to a support vector machine prediction function.3 The graph-derived context variables improved the fraud prediction by approximately 20%.

Clearly, the benefits of adding graph-based predictors are better lifts and lower rates of false positives. The improvements range from 20% to 100% depending on the richness of data available today. These benefits translate directly into bottom line results to the extent that your company is already putting predictive analytics into daily operations. So what are the practical aspects of putting this into operation?
How Do You Get Started?
Let’s start with the data: Where do you obtain the data to construct such contextual networks? The IBM study looked at the anonymized email traffic, address books and buddy lists. If you were a retailer like Amazon, you could use wish lists, product gift transactions and share-the-love data to construct the social context for predictions. Financial services companies can use the trading data to construct the contextual network among the traders or credit application data to construct an information network. Telecommunications companies can leverage the call detail records. In fact, the explosion of data collection means that most organizations already have this contextual network information just waiting to be accessed and utilized.

One unique aspect of contextual network predictors is that they are usually specific to the target outcome you want to predict. This is the definition of contextual after all. So if you want to predict churn, then you need to understand how the contextual network predisposes a subscriber to churn. Such contextual churn insight will certainly be different if you were trying to predict employee revenue or product adoption.

Since the contextual environment is changing, the window that an enterprise has to leverage the additional predictive power of contextual networks is also dynamic. Most customers will switch vendors very soon after they learn about a bad experience from a friend, so vendors must respond in a timely manner. Practically, this means that the calculation of graph-based predictors must occur more frequently.

Some contextual network information is relatively stable over time. For example, if someone has a preferred takeout restaurant, she will likely continue to call that restaurant even after she gets a new phone number. These “creatures of habits” can be more easily identified, say in the case of fraud or terrorism prevention.
Challenges
One potential impediment to leveraging contextual network information involves the privacy and data security issues. Since contextual networks tend to involve personal transactions, companies would like to ensure the highest security to prevent breaches. Various regulatory bodies at different state, country or global levels might have conflicting guidelines on what information is considered private and cannot be used for data analytics.

Another obstacle today is the scalability challenge. Often these contextual networks are many times the size of the current data used for analysis. Contextual networks rely on relationship information, so sampling runs the risk of losing valuable information. James Kobelius of Forrester postulates that the advent of social network analysis will catalyze the need for petabyte-sized data warehouses.4

Another obstacle is the lack of software tools that will generate these graph-based predictors. Types of specialized graph based calculations include community detection, diffusion simulation, vertex similarity, topology and centrality analysis. Open source tools such as Jung and Pajek work well for R&D, while proven commercial tools such as Sonamine are geared toward large scale and production graph mining.

A last obstacle is education. Contextual network predictors are unlike other types of data being captured and analyzed. Enterprises must first capture this new data, and that will require some mind-set shift. Setting up an infrastructure to model the contextual network will require new tools and approaches. Finally, using these contextual networks in predictive analytics that improve the bottom line will require the cooperation of multiple disciplines—analytics, marketing and operations.

Despite obstacles, the time to investigate and leverage these graph-based methods is now. IDAnalytics, a company that provides identity theft protection, has been issued a patent for a system and method for identity-based fraud detection through graph anomaly detection. How will you start adding graph-derived predictors into your predictive analytics?

To benefit from Predictive Analytics, there are potential pitfalls to avoid along the way. In this interview with Vijay Desai, Ph.D., Principal Scientist, SAS, Dr. Desai shares his ideas and solutions as they compare across multiple industry segments.

Transcript:

Bill Cullifer: I am on the phone with Dr. Vijay Desai, Principal Scientist for SAS, S-A-S. Dr. Desai, good afternoon and thanks for agreeing to the call.

Vijay Desai: Thank you for having me.

Bill Cullifer: You bet. You recently participated in a panel discussion entitled Cross Industry Challenges and Solutions regarding predictive analytics and the theme included hurdles to jump and barriers to avoid when considering benefiting from predictive analytics. Can you summarize that discussion and what you walked away with?

Vijay Desai: Sure. Some of the things that we discussed were #1 analytics is becoming more and more useful in different industries and so as we go from industries where analytics has been used for a long period of time, in new industries there are some challenges. Some of these challenges include first convincing the users that predictive analytics can be of help, that predictive analytics is not going to take away their jobs, but by increasing their productivity it’s going to make them more valuable and help them generate more value and then even the data is becoming more and more available in different industries, you do not have what I would call targets. So, for example, in the case of fraud, you… target would mean identify the accounts that are good or fraudulent accounts. So, when you do not have targets available, it makes your job more difficult. So, in credit card fraud, targets are easily available because if your credit card gets stolen, you will report to the credit card company pretty soon that it has been stolen and so the credit card company can tag your account as being in a fraudulent state whereas in other industries where we are trying to use predictive analytics now say for example tax under filing, the tax under filers are not going to report that, “Hey, I am under filing my taxes this year.” So, you do not have that target information. Also, to take things further if you are… I have also worked in the area of network intrusion detection where you are monitoring a computer network, you don’t know which particular activity is malicious and you know which one is normal. So, again, you do not know the targets. After the fact, using forensics, you can identify some of the activities as being malicious or not, but you still are not sure you know whether you caught all the malicious activity or not and then going even one step further for bio surveillance; fortunately, we do not have any examples of bioterrorist attacks and so when you are building models, when you are trying to use predictive analytics for something like bioterrorism or bio surveillance, it is a much more of a challenging problem.

Bill Cullifer: Thank you. I certainly appreciate your perspective and for your time today.

Vijay Desai: Most welcome.