Short Message Service (SMS)
Remaking Business Intelligence
By Brendan B. Read,
Senior Contributing Editor
For contact center agents, retaining customers and improving sales starts with data-gathering. And
business intelligence – or “BI” – is one technology that helps turn data about customers into improved
retention rates as well as profits.
Having solid BI methods and tools in place is becoming critical for firms what with forecasts showing a slow recovery, including with it marginal but growing increases in spending. Companies need to extract every last ounce of value from the
data, obtaining from it a most accurate picture of what is happening with customers. This way they can fine-tune with customer relationship strategies to obtain maximum revenue at minimal costs.
At the same time firms need to look at refining their BI in view of changes in how customers interact and transact with organizations. They are using growing array of channels both traditional live-person (agent via voice, e-mail and chat, and retail/counter), newer automated (Web, kiosk, and IVR self-service) and the latest: social media. That means firms have to look at hard at the types of data that can be collected from these interactions, which could force them
to change how they aggregate, analyze and process them. The payoff in having the right BI methods in place is enabling companies to stay competitive or better yet, best their competition in today’s environment where there has been a commoditization of many products, pricing and even service.
“You can relatively easily gather for example that some of your customers are satisfied dissatisfied from surveys,
“says Boris Evelson, principal analyst, Forrester. “But to automatically andproactively infer the reasons for their satisfaction or dissatisfaction from their activity in their account, e.g. all of a sudden they start withdrawing
Boris Evelson, principal analyst, Forrester, offers these best practices when considering and deploying BI:
1. Place a broadly defined BI process, one that goes beyond dashboards to include analytics front and center of all CRM activities
2. Insist that businesses, in partnership with IT own CRM data and metrics “If firms look at BI as just another technology and tool and relegate management of BI activities to IT department that is a sure recipe for disaster,” he points out.
3. Practice top-down CRM performance management (definestrategy; define supporting goals and objectives, and identify metrics needed to monitor goals and objectives “Say a customer satisfaction goal is a churn rate that is no more than X percent per customers per month,” explains Evelson. “When you agreed on those metrics and how those metrics support those goals and objectives then you can analyze the data sources to understand why that churn is occurring and amongst which customer segments.”
4. Create BI applications and processes that support all types of CRM decision makers: strategic, tactical and operational “In CRM there is an interplay between strategic decision makers versus operations decision makers, i.e. ‘I’m on the phone with customers, I need to make decisions right there on the spot’ versus ‘I need to analyze customers’ buying patterns to optimize service to them, “says Evelson. “Yet the architecture and tools needed to support these strategic versus operational decisions are very different.”
5. Use BI not just to monitor and analyze customer behavior but for internal CRM processes, success or failure. Use BI to adjust CRM strategy and tactics as necessary.
money, or when they complain on Facebook, require a comprehensive BI application. Having that capability is what differentiates companies from each other. “If you can make a better, faster decision on information with it [BI] from your customers than your competitors guess which one is going to respond with the most success? And when your analytics does cover social media as an extra dimension and are more insightful then obviously you will make better decisions and that will be your competitive edge.”
Freeing the CRM Data
The biggest need and driver for BI is to unlock the power of the data aggregated in CRM systems, just as basic oxygen furnaces tap the strength of steel hidden unprocessed inside iron by applying the right elements and processes.
Brad Peters, CEO of Birst, is seeing huge demand for analysis of CRM data. Companies have made tremendous investments in their CRM systems over the past decade, and now want to see how they can make the most of that information. The reporting available within the CRM solutions is often rudimentary and can hamper the CRM solution itself, though, so companies are turning to BI for better, faster, and more effective analysis.
“At its core, BI allows customer-facing organizations to treat their customers strategically,” says Peters. “This means arming customer-facing employees with the power to make better decisions at the time of customer interaction. By driving better customer interactions (as opposed to just more efficient ones which is really the goal of CRM), customer experience and customer value can be dramatically impacted.”
Mark Flaherty, vice president, marketing, InetSoft, says there is a huge amount of insight about customer behavior and potential churn or customer loss to be derived from all the data stored in a CRM system that goes otherwise untapped without decent BI tools. Businesses know this; in the past, they’ve looked to copy the data into data warehouses and use heavy duty statistical analysis software to develop retention and scoring models. But now, while that is still important, businesses are looking to make the operational data in CRM systems more accessible and to link those retention scores back to the customer accounts.
“The biggest problem with retention management is that by the time an at-risk customer is recognized using traditional methods, the customer has already left, and getting him or her back is either impossible or exorbitantly expensive,” explains Flaherty. “With a BI tool that offer good visualization capabilities, now macro retention trends can be spotted more easily and quickly and even at-risk customers can pop out at a glance. Front-line business managers are able to react sooner instead of waiting for statistical analysts to spit out reports for them.”
There is still a challenge in using BI tools with CRM systems that are closed, storing the data in proprietary databases. This makes accessing the information difficult.
“I think CRM users are getting fed up with that, and [as a result] more vendors are opening access to the data,” says Flaherty.
Forrester’s Evelson thinks the BI scope should be broadened to include data gathering and management including data cleansing, integration, aggregation and warehousing to enable the information to become meaningful actionable and integrated. BI has too long been defined, he says, to only encompass analytics, dashboards and reporting. Some 80
Business Intelligence and Predictive Analytics
When looking at employing or refining BI take a close look at coupling predictive analytics solutions to it. By applying predictive analytics software to the data already harnessed through BI technology, organizations can uncover unexpected patterns and associations and develop models to guide front-line interactions with their most valued assets: customers, constituents or employees.
BI and performance management focus on what’s happening now or happened in the past, explains Erick Brethenoux, vice president of corporate development at IBM SPSS.
Why these occurred, what is likely to happen next, and forecasting future trends is what predictive analytics delivers. It does so by analyzing, modeling and scoring demographic data (attributes) and transactional data (actions) from operational systems, as well as attitudinal data gathered through customer feedback and surveys. Predictive analytics provides specific, real-time recommendations on the most appropriate decision to make and/or action to take at that precise point in time.
IBM is coupling BI and predictive analytics into upcoming offerings through its acquisition last year of SPSS. While IBM’s BI solutions enable organizations to understand performance and make better decisions, IBM SPSS predictive analytics software capitalizes on that insight, allowing organizations to anticipate change so that they can acquire, grow and retain customers, while mitigating fraud and reducing risk.
“The combination results in a distinct competitive advantage, allowing organizations to optimize all parts of their business,” says Brethenoux.
percent of the effort in building CRM is data organizing, reconciling from different sources, and sorted in different formats, addresses, and identifiers.
“If I am a business, I need to know if I am selling into the right or wrong customer segment, how customers are responding, and customer satisfaction,” says Evelson. “Unless there is a very strong analytics program on back end with sufficient and clean data in BI, I cannot see what’s effective, and what’s not effective.”
Crossing the Channels
The growth in Web and IVR/speech recognition and ATM/kiosk self-service, mostly at the expense of live-agent and retail interactions has created a need for companies to also deploy analytics across these channels to understand customers’ behavior. The information obtained is being integrated with that pulled from contact centers and retail via BI to see and predict customers’ actions.
Crossing those channels can be challenging though. By definition self-service does not capture the customers’ tone, which imparts invaluable information as effectively as speech analytics on live agent calls. Also, the information
often rests in different silos. That runs the risk of inaccuracy: multiple entries and storage increase the odds of data errors such as misspellings and incorrect addresses that annoy customers.
“So how can firms optimize the use of BI tools and minimize data quality issues given the multichannel environment and limited direct exposure to customers?” asked Vuk Trifkovic, senior analyst, Ovum. “I’d recommend: a) data quality initiatives; b) master-data management (MDM) initiative; c) a knowledge-management layer; and d) unified approach to customer intelligence.”
BI and Social Media
If employing BI to process information and gain insights from customer interactions in existing channels: contact centers, IVR and Web self-service and retail was challenging enough, then social media threatens to knock managers for a loop. Social media is unlike any other channel in that it is not one-on-one but a community, one where customers not the companies control the messaging. Customers can therefore make or break a product or offering at the speed of light by their comments.
Tapping into and managing information from social media is also different from other channels. These rely on and BI apps pull from structured data whereas social media data is unstructured and flowing between multiple parties. Firms cannot just use standard text-parsing tools because the terminology is different for different industries and verticals and regions, and social communities. They must create custom applications to understand the meaning of extracted keywords that they can then integrate them into the BI applications.
“Anyone can write a program that would take an e-mail or IM and chop it up for keywords,” explains Evelson. “That is not enough in social channels. It requires a much more sophisticated semantic entity fraction; to understand which keywords are the subjects, objects and actions. You have to ‘train’ these applications to understand the taxonomies that could be unique to each industry or business domain.”
To tap into social media, Evelson recommends that firms tighten down their existing BI processes i.e. walk before you can run. They then need to understand what their problems are in the social channel, and set up goals and objectives and metrics in response.
While firms, chiefly their marketing departments are beginning to listen to the social channel and in some cases use it for brand awareness and respond to comments and issues. Yet a much more difficult task is to actually adjust strategy, tactics, campaigns and product offerings based on insights derived from social media.
“Even if I glean some interesting insights from social interactions how do I tie that to back to the cross-sell/upsell ratio and customer satisfaction; it is not a one-to-one relationship,” says Evelson.
Ovum’s Trifkovic says that BI tools per se can readily manage social channels through natural language processing built-in, taking advantage of the text-based nature of interactions. Also, social networking can be graphed to understand who is saying what and who are the leaders and follower on social channels via the relatively well understood concept of network analysis.
The challenges lie in normalizing data across channels, establishing social media participants’ identities – which are often nicknames – and how best to monitor these interactions track what is being said about companies. These also include how to effectively flow through and incorporate loose unstructured data from that channel. Also, the volume of data pulled in from social media consumes is large; it may require firms to bolster their processing capacity.
“The BI framework is there but the analytics are not quite there yet,” explains Trifkovic. “There needs to be more network and semantic analysis, and perhaps more parallel processing to handle the information. While enabling BI for the social channel will require frankly a little bit of vision, case study but in general I don’t think it is going to be an insurmountable problem.”
The following companies participated in the preparation of this article:
SPSS (an IBM company)
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