Predictive Analytics Today

Accessing A Wealth of Data

In today’s technology-rich business and consumer environments, vast amounts of data are generated through personal (social networks, mobile devices, Web, store kiosks), societal (geolocation, images, media) and industrial (digital sensors, automation) interactions.

Many organizations would like to access the data gathered through some of these diverse sources to uncover valuable information related to customer opinions, habits and preferences. However, due to the volume, velocity and variety of unstructured information, many businesses face key challenges when trying to capture and analyze this data.

Fortunately, analytic processes are available that enable companies to retrieve that information, gain vital ROI insights in the process and deliver real-time targeted communication with customers. Moreover, companies can now leverage that data to learn about customer needs and opinions in ways not previously possible, gaining a competitive advantage and increasing their bottom line.

These Customer Analytics tools have a profound effect on how organizations acquire customers, increase lifetime value and identify consumers who offer the most value as brand advocates, to name a few. These solutions enable the types of interactions crucial to deploying targeted retention efforts, making it possible to determine the best focus for a company’s marketing goals.

Customer Analytics Defined

In general, Predictive Analytics captures unstructured and structured data, uncovers hidden patterns and associations within that data to determine future outcomes, and acts upon the insights gained through optimized, real-time decision making.

More specifically, Customer Analytics gains deep insights into customer attitudes and preferences to predict future behavior and deliver a 360-degree view of the customer throughout their lifecycle. Combined with Business Intelligence (BI), this deep customer analysis can be shared through dashboards, scorecards, and other visualization tools to convey results along with key performance indicators and predictors.

Customer Analytics comprises four key areas:

• Customer Acquisition offers tools for businesses to attract new customers by employing capabilities, such as segmentation, clustering techniques and targeting for companies to match their business offerings with what customers want. This enables more accurate targeting strategies while reducing wasted advertising costs on those unlikely to accept an offer.
• The importance of effective analytics tools for increasing Customer Lifetime Value and making offers more accurate and unique cannot be understated. By using Customer Analytics to create 360-degree consumer portraits, and then personalizing communications, companies can up-sell and cross-sell to those clients to maximize profitability and increase customer lifetime value. Businesses are not only increasing revenue, but maintaining and developing crucial relationships with their customers
• Customer Loyalty & Retention enables companies to calculate customer value and to determine whether the retention effort for that specific client is worth the investment.

  Loyalty is not only concerned about rewarding customers with personalized offers through loyalty programs, but also with turning satisfied customers into successful brand advocates. Techniques such as social network analysis enable businesses to identify those customers with a significant sphere of influence among their peers. As customers’ trust in traditional marketing messages has been steadily decreasing, the ability to leverage peer recommendations by capitalizing on those active in social media is a powerful way to enforce brand loyalty

  Retention allows companies to identify early warning signs of defection. Companies can proactively approach the best customer to target based on likelihood to accept an offer to stay and not defect to a competitor.

• An emerging area, Social Media Analytics unlocks the value of customer sentiment. It functions both as a listening tool and as a means for predicting consumer behavior and improving customer satisfaction. By incorporating valuable unstructured data into analysis, Social Media Analytics functions as an integral part of all marketing strategies for achieving actionable ROI.

[Download PDF to see Figure 1]

Current Pain Points

Today, a significant number of companies understand the importance of responding to customers in real time. Yet they still rely primarily on transactional and demographic data for strategic marketing information.

This is one conclusion among many related to Customer Analytics from an August, 2012 UBM TechWeb State of Loyalty and Retention Strategies research project that examined the role of analytics technology in today’s business enterprises.

The Untapped Value of Social Media

A majority of companies in the UBM TechWeb poll (57%) expressed a strong desire to incorporate technology-based customer loyalty tools, such as Social Media analytics, into their marketing strategies.

While integrating and understanding demographic and transactional data is important, what’s equally critical is to include interactional and attitudinal data—often in the form of unstructured data, like social media or customer surveys. Combining all this information together provides companies with an invaluable 360-degree view of the customer.

By actually capturing unstructured interactional data from different Social Media outlets, and performing sentiment analysis on key words and terms, companies can gain deep insights into customers’ attitudes and preferences, uncovering true customer feelings that ultimately drive business value.

[Download PDF to see Figure 2]

A number of respondents in the study cited their current Business Intelligence tools as pivotal in terms of improved customer understanding. These companies also considered Customer Analytics adoption as highly desirable (59%), and more than half of the businesses (51%) in the study consider themselves strongly proactive in terms of incorporating new analytic technologies.

Particularly, the survey respondents’ recognize the value of Social Media analysis for gaining deep understanding and insight into customer behavior, with many noting that a future goal is to acquire the capabilities to analyze social media data to uncover customer sentiment.

The UBM TechWeb poll results also show companies recognize that analytics tools such as Customer Analytics offer the capabilities to assess and analyze that data to gain consumer insight and to turn satisfied customers into profitable brand advocates.

The Challenge of Customer Retention

It’s become an accepted fact that most customers no longer trust traditional marketing messages. Moreover, in the current economic climate, a majority of consumers not only search for the best offer, but their loyalty to a specific product or company is often fleeting. On the other hand, businesses understand that profitability accrues through loyal customer retention as opposed to new customer acquisition.

Further, in the UBM TechWeb survey more than half of these technology professionals (53%) stated that long-term customers represented a highly profitable market segment. However, successful customer retention requires that companies turn their regular customers into satisfied customers and then into loyal advocates. Proactively reaching out to customers before they defect to a competitor requires effective Customer Analytics capabilities.

To achieve these proactive customer interventions, businesses employ Customer Analytics to mine data for invaluable customer sentiment. Accessing this demographic, behavioral, interactional and attitudinal information, companies then identify the best offer using the right channel at the perfect moment to keep that customer from defecting.

These companies can deliver targeted, real-time offers to customers via the optimal touchpoint for that individual customer, like email, social media, or a phone call, to maximize customer value and proactively prevent defection.

Such capabilities often mean the difference between holding on to customers or inadvertently driving them away due to an ill-timed retention offer. With Customer Analytics, businesses achieve a level of granular insight into a chosen customer base.

They can then uncover the specific buying patterns, and timed offers, that lead to a satisfied customer. A successful analytics solution helps these companies take steps toward proactive retention for those customers who are showing early signs of defection.

[Download PDF to see Figure 3]

Technologies

How Predictive Analytics Helps Tame Big Data

In general, organizations rely on a range of transactional and demographic data to uncover customer trends and buying patterns. To augment these capabilities, sophisticated analytics solutions have been created to help companies grapple with an increasingly complex consumer marketplace.

From improving customer insight to waging loyalty campaigns and implementing retention strategies, businesses are presented with a range of challenges—and a wealth of tools—from which to choose.

Today’s Big Data requires sophisticated analytics that use iterative analysis and pattern recognition, not to mention the ability to handle the sheer size of data workloads with which businesses must now contend.

Often, many companies end up not using valuable customer info because they don’t think they can access it or act upon it. However, a percentage of these companies recognize that Customer Analytics adoption can accelerate the integration of unstructured data that exists beyond the four walls of their datacenter with traditional data types.

Further, integrating this rich data will facilitate personalized, one-to-one customer experiences and real-time responses that enable these companies to make appropriate business decisions that will have a profound effect on their bottom line.

In the final analysis, the importance of taking a proactive approach to customer interventions versus reactive strategies is clear. Utilizing multiple channels and opportunities for interaction reinforces this approach. In fact, 74 percent of respondents in the UBM TechWeb poll cited customer feedback as critical to their Loyalty and Retention efforts.

Further, more than half (58%) placed a strong emphasis on detecting early warning signs of customer defection. Without the types of proactive solutions enabled by Customer Analytics, business owners realize they leave themselves at the mercy of unforeseen customer defections rather than taking proactive steps toward retention from a strong analytics-based foundation.

When it comes to integrating new technology solutions such as Customer Analytics into their customer outreach and evaluation toolset, companies see the value of adoption. In fact, more than half of the UBM TechWeb respondents currently consider themselves proactive in terms of incorporating these tools into their marketing approach. Moreover, they understand the impact of staying ahead of the Big Data curve in a profoundly challenging and competitive global marketplace

IBM Predictive Analytics

Taking the “Bark” Out Of Big Data’s Bytes

IBM Customer Analytics offers advanced, scalable tools to handle the large data sets that enterprises increasingly encounter today. These solutions are also flexible enough to grow in analytical sophistication as smaller businesses expand and develop. Companies can incorporate these analytic capabilities and avoid being overwhelmed by today’s wealth of information or increasing customer demands.

Combining predictive analytics and Business Intelligence, IBM Customer Analytics software offers its solution based on the “Align/Anticipate/Act” modality. This marketing model illustrates the three critical actions that enable businesses to attain successful customer interactions:

1. Align: Gather & capture data from diverse sources
2. Anticipate: Discover trends & patterns within data to enable customer behavior predictions
3. Act: Achieve optimal decision-making based on key research results

This model enables companies to incorporate all the data they accrue and to understand, at a granular level, how customers will best respond to their various offers and retention outreach. IBM Customer Analytics helps businesses realize they have the ability to place structure around unstructured data to achieve actionable results:

- SPSS Data Collection

A data capture tool, SPSS Data Collection, is an example of a key IBM survey management product. It pushes information out to consumers through the creation and deployment of surveys.

This tool offers a means for proactively reaching out to customers to rate the quality of products or services. Methods range from survey creation and deployment to reporting of survey results. Then results are compiled for attitudinal information and sentiment analysis which businesses leverage to complete a 360-degree customer profile.

- Cognos Consumer Insight

Cognos Consumer Insight (CCI) is a social media analysis tool that captures sentiment from diverse Social Media sources, such as Twitter feeds, Facebook postings, mobile apps, Web page and analyzes it to uncover customer sentiment.

Business users can determine which key words and concepts are generating social buzz. Sentiment analysis indicates if the words or concepts were being discussed in a positive, negative or neutral tone. This analysis can be used to segment a customer base, monitor brand perception, and gain deep insight into customer feelings.

- SPSS Statistics

IBM SPSS Statistics offers linear and non-linear data models to identify trends and create accurate forecasts. Simulation capabilities offer users the ability to perform “what-if” scenarios.

Users can better understand data with customized tables that provide capabilities to test hypotheses, validate assumptions and make better business decisions.

- SPSS Modeler

SPSS Modeler is a sophisticated data mining and predictive modeling workbench that can gather diverse types of structured and unstructured data, then drill down into that data to uncover hidden patterns and correlations.

For example, it can detect customer dissatisfaction related to a message or product. By performing sentiment analysis using text analytics, the software analyzes the data and provides a tangible score to alert business owners to the level of customer appreciation or dissatisfaction.

-IBM Analytical Decision Management

Delivering actionable recommendations in real time is a key concern for business leaders. IBM Analytical Decision Management combines predictive models with business rules and optimization to deliver key recommendations.

For example, the software helps guide employees in call centers and in other related systems, to make the right business decisions to increase conversion rates or customer purchases. Business rules function as recommendations for the offers a call center support person would apply to this client to make them stay. That optimized decision would be delivered to a customer most likely to defect. IBM Analytical Decision Management facilitates delivering those valuable business offers in real time.

Conclusion

As business professionals assess the critical infrastructure necessary to cope with the Big Data onslaught, one component is key—comprehensive Customer Analytics adoption. These analytic solutions function in two ways: they extend out to the customer base on which businesses rely, and back to harnessing the powerful resources of today’s data center.

From accessing rich Social Media data to increasing customer Loyalty & Retention, businesses today have a number of goals to focus on at once. A key question remains: How are various departments and business leaders viewing the role of technology in assisting them to accomplish their loyalty and retention tasks?

According to the UBM TechWeb State of Loyalty and Retention Strategies survey, they’re actively taking charge and looking for the best diagnostic and analytics tools to help them achieve their goals.

Moreover, these companies realize that by implementing proactive loyalty and retention initiatives, they can better understand who their satisfied customers are and turn those customers into successful brand advocates.

To achieve measurable results in the area of Loyalty and Retention, companies need to leverage the available resources and tools to improve communication with, and learn more about, their customers. Proactively incorporating additional data sources, and accessing Customer Analytics tools to analyze this information, represent key future trends.

Otherwise, businesses risk falling behind the Big Data curve and missing out on Predictive Analytics adoption— solutions that offer the power to maximize the value of existing customers, interact with them in real time and capitalize on the increasing value of retention strategies.