Predictive analytics and crm


Customer Relationship Management software was a rage in the late 1990s but enthusiasm for it began to wane in the 2001-03 period. An estimated 50% of the CRM projects did not yield a payoff. While CRM was admittedly a fine tool to keep track of account and transaction information, it did not yield a profit due to its inability to contribute to actionable conclusions. The picture began to change when predictive analytics was added to the menu of functions available with CRM. Increasingly, companies recognize that predictive analytics is an indispensable tool for decision-making.

Predictive analytics has proved to be valuable in an environment where uncertainty has increased as a result of a wider array of means available for companies to promote their products and services. Cars, for example, can be promoted by showcasing them in malls, advertising in a glossy magazine or on network or cable television. Increasingly, companies are realizing that they need data on customer responses to each of these means of promotion.

They also have more options for channels, media, services and products that they can offer. These options are often inter-dependent and companies need to plan how they can work cohesively. For example, new products, where customers need to learn new features, are better promoted by call centers supported by technically adept staff. Countless communities on the web present opportunities for companies to advertise to their target audience. Companies have to find the means to evaluate each of the options they have to sell their products and find the data to measure their effectiveness.

Customers also are able to articulate their individual needs are not satisfied with the staples that were common in the past. Companies need information on behavioral traits of customers that underlie their distinct needs so that the most relevant products and services are offered to each segment of the population.

Mass marketing is a costly means to promote products and overwhelms customers who are exposed to the din of growing numbers of marketing messages. People are so tired of advertisements that they are not paying attention. A recent study by Yankelovich Partners, a marketing-services consultancy, found 65% of people now feel they are swamped by ad messages and 59% feel that ads have very little relevance to them. Almost 70% said they would be interested in products or services that would help them avoid marketing pitches. Unsurprisingly, a recent Deutsche Bank study of effectiveness of TV advertising on 23 new and mature brands of packaged goods and concluded that there was a positive cash flow in 18% of the cases. Over a longer term the picture improved, with 45% of cases showing a return on investment. Much of the positive cash flow was accounted for by new products suggesting that innovation was more important than advertising.

Predictive analytics helps companies to evaluate the most cost effective channels and media as well as the communication messages for specific segments of the population. Companies that excel in marketing, such as Gillette and Pepsi, who in the past relied greatly on television advertising, have recognized that tech-savvy 12-24-year-olds do not respond to television as well as the ageing “baby boomers.?Recent product launches of Code Red Soda (Pepsi) and Venus Razors (Gillette), meant for young women, changed their tactics and reallocated at least half of the marketing dollars from television to interactive games, viral marketing programs and media that this younger generation enjoyed.

Gillette placed Web applets on teen sites to draw the elusive teenage girls (as they begin to shave) to learn about and interact with this new brand. Pepsi realized that technically sophisticated young men are drawn to interactive game contests and it could attention by offering cases of the Code Red soda as rewards to the winners. Code Red was able to achieve the sixth highest soda sales (2.2% share) in convenience stores with relatively little television advertising compared to other Pepsi product launches.

Predictive analytics goes beyond the traditional CRM methods to find the patterns in the data that is collected. It identifies segments or affinity groups among customers, it seeks to determine the causes of observed patterns of purchasing behavior and evaluates the results of marketing campaigns to target customers more efficiently in the future. The analysis helps to identify specific channels and media most relevant for individual segments of the population which lowers the costs of acquiring new customers and to retain them.

CenterParcs, a European travel management company, believes it can forecast when a customer will book a holiday, its location and the duration of the stay. It is able to foretell travel behavior by using its predictive analytics software which has helped it to reduce its direct mailings to a quarter of the earlier level even as it has increased revenues at the same time. It also claims an average occupancy rate of around 90% in its holiday homes around Europe.

None of these techniques would be useful without large volumes of data for numerous series. CenterParcs, for example, draws on more than 100 million customer records dating back as far as 1982 and combines these with external data sources such as demographic or geographical information. It uses information on 60 to 80 variables to estimate the probability of them booking a holiday with them for a particular destination at a certain time of the year.

Companies can use a broad range of techniques to predict outcomes with increasing accuracy. While numerical data was common in the past, large quantities of textual information can also be used now to predict consumer behavior. This is particularly useful to identify the pain points that help to find new product and services. A great deal of the data received by call centers, for example, is in the form of conversations with customers. In terms of analytical techniques, companies have a choice between artificial intelligence techniques such as neural networks which find patterns in the raw data. On the other hand, linear regression models are useful for finding causation and prediction. Techniques such as logistic regression can estimate the odds of a customer buying a product or the risk of default. Finally, methods such as time series analysis find patterns in data over a period of time.

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