Predicitve Analytics Essay Example

a particular time, with a particular customer.

The result is more effective customer relationship management strategies, including advertising and marketing campaigns; upsets and cross-sell initiatives; and long-term customer loyalty, retention and rewards programs. Current market situation Most BIB companies which tries to get deeper customer understanding and move segmentation beyond traditional way using selects from Industry, size, anemographic views of customers is not reaching up to the standard.

In a top business marketers in the United States, themes pressing concern identified by respondents was "finding a better way to expand understanding's their customer needs, market segments, and the key drivers of customer value. " Companies which have traditionally relied on technological innovation to attain competitive advantage have come to realize that new technology or new product features are not good enough to attract more customers or increase revenues from existing customers. Major challenges 1 . Sales cycles are long and complex offerings. 2.

Competitor's offerings and strategies shift so quickly that managers cannot reliably compare the impact of changes in a given marketing 3. Customer relationship management systems cannot easily capture the decisions and actions that led to success or failure with any particular account, because such information is largely anecdotal, not quantitative. The following table represents some examples of the types of challenges solved by predictive marketing for different types of digital marketers: Benefits or Strategic objectives Attained through Predictive Analysis

The predictive approach not only produces forward-looking segments; it also gives users a high degree of confidence in their decisions about what to do differently for each segment. By scientifically testing how customers might respond to future offerings, channels, and pricing; companies know

how to reach the right customer with the right offer at the right time, through the right channel. 1. Compete - Secure the Most Powerful and Unique Competitive Stronghold A predictive model distinguishes the micro segments of customers who choose your company from those who defer or defect to a competitor.

In this way, your organization identifies exactly where your competitor falls short, its weakness. 2. Grow - Increase Sales and Retain Customers Competitively Each customer is scored for their behaviors like purchases, responses, churn and clicks. These scores drive the enterprise operations across marketing, sales, and customer and help the organization to have competitive advantage Aberdeen group in August 2011 (Predictive Analytics for Sales and Marketing: Seeing Around Corners) found that companies using predictive analytics enjoyed a 75% higher click through rate and a 73% higher sales lift than companies that did not SE this technology.

Figure below shows the details of the research conducted among 160 test audiences. (Source from:- Aberdeen group in August 2011 -Predictive Analytics for Sales and Marketing: Seeing Around Corners) ranking transactions with a predictive model dramatically boosts fraud detection. 4. Improve - Advance Your Core Business Capacity Competitively Whether offering a service or a product, enterprise's central function is to produce and deliver with increasing effectiveness and efficiency. By way of greater efficiency would be able to overproduces/services at cheaper prices. Satisfy - Meet Today's Escalating Consumer Expectations By offering very targeted offers that have more probability of acceptance. Companies are able to accomplish their marketing objectives and set the customer expectation without increasing their marketing staff or budget. Business application of predictive analytics Most of the organization

applies predictive analytics to automate operational decisions, across marketing, sales areas and beyond. Choosing the business application of predictive analytics depends on strategic question or type of decision companies choose to automate.

Companies run variety of campaigns to accomplish specific goals, such as acquisition, cross-selling, and retention. Predictive analytics creates a range of models, parallel to their business application; table below shows some of the business application and the predictions that companies look forward. Business application: Predictions Customer retention customer defection/churn/attrition Direct marketing customer response Product recommendations what each customer wants/likes Behavior-based advertising which ad customer will click on Email targeting which message customer will respond to

Credit scoring debtor risk Insurance pricing and selection applicant response, insured risk Supply chain optimization 1 . Supply chain visibility and cost to serve 2. Demand forecasting Optimization 3. Network optimization: is about analyzing total cost of ownership of a company's supply chain network. 4. Predictive asset maintenance: improving up times, performance and availability of manufacturing assets by predicting when maintenance or when a new part is required in order to avoid unplanned down time. . Spend analytics: understanding how much a company is spending on different recumbent categories, with which suppliers, and how a company can optimize their spending across all those categories. Conventional campaign approach In traditional campaign approach markets typically use a few basic selections to identify customer behavior while creating a campaign. It was mainly based on internal company processes, rather than focusing on the needs and preferences of its customers.

Response to these types of conventional campaigns is generally low often less than one or two percent. Optimizing campaigns with Predetermination In order

to optimize marketing campaigns, companies need to be able to answer the four crucial questions like Who should I contact? What should I offer? When should I make the offer? How should I make the offer? Predictive Marketing enables marketers to find the answers quickly, and to create and execute campaigns around this simple but effective process.

First, marketing analysts create predictive models; as we have discussed earlier creating models depends on the business application or strategic question in hand companies. These models helps to efficiently find appropriate customers and discover the best timing,channel, and message for each customer. Then, arresters add business information such as contact restrictions, budget guidelines, and campaign objectives. Before sending the campaigns, they verify the projected size and cost of each campaign, as well as the expected response and revenue on each campaign.

Finally, the marketers execute the approved campaigns. Select the right audience Using the model campaigner decides the right customer segments to send out the campaign; deciding the target segment using the model typically reduces campaign costs by 25 to 40 percent, while maintaining or even increasing response rate. Select the right channel At this stage of the campaign process, marketers determine how best to contact each customer. By using each customer's preferred channel, (based on channel preferences and predicted response) companies increase response rates.

Select the right time Consumers today have many choices for meeting their needs. That's why it's critical to reach customers in a timely manner when their behavior indicates an unmet need or a risk of defection or attrition. Predictive Marketing continually scans customer databases for Just such events, and triggers specific campaigns when

a need or risk is detected. Some companies increase the frequency of campaigns to improve the chances of reaching customers at an ideal time. These campaigns target fewer customers, but the customers they do target have a high likelihood of response.

When the campaigns are finished, they use Predictive Marketing to compare actual results to the projections, and incorporate information that can improve the effectiveness of future campaigns. This process is accomplished in Predictive Marketing two main modules, the Analytic Center and the Interaction Center anticipate the needs and preferences of individual customers. The Interaction Center s used to create, optimize, and execute campaigns based on the customer needs predicted by models created in the Analytic Center.

Together, the Analytic Center and the Interaction center enable companies to answer the "who, what, when, and how' of successful campaign marketing. Marketing analysts create predictive models of customer behaviors and preferences in the Analytic Center. The models are then used by marketers to create and optimize campaigns in the Interaction Center. New interaction data is sent back to the Analytic Center to refine and enhance the predictive models. Select the right offer When companies increase the number of campaigns they run, they risk alienating their customers by overloading them with offers.

Conventional campaign management tools are not designed to address the potential overlap. Predictive Marketing, however, reduces this risk through a comprehensive campaign optimization process. Predictive Marketing evaluates all of the available campaigns and selects the one that best balances the customer's likelihood to respond with the profit potential of the campaigns. It also takes into account suppressions and contact restrictions, such as "do not call" or

"do not contact more Han once every two months. This customer focus, combined with the ability to optimize campaigns around restrictions and preferences, has enabled companies to report a profit increase of between 25 and 50 percent. As companies transition from large, unfocused marketing campaigns to highly targeted, event- based campaigns across multiple channels, their marketing departments go through several stages Predictive Marketing enables companies to run more effective campaigns at each stage of the transition. Stage 1: Right customer 2: Right channel 3: Right time 4: Right offer 1 . Objective

Select the targeted customers For each campaign Select the best channel for each customer Contact each customer at right time Select the best offers for each customer 2. Enabling technology Predictive analytics Channel optimization Event marketing Campaign optimization 3. Strategy Predict who is likely to respond to a campaign and balance that information with against expected revenue Balance each customers channel preference against triggers to select customers Balance the customers likelihood to respond against the profit potential of each campaign 4.

Benefit 25 - 40% reduction in direct marketing cost Decreased cost of Interaction Up to double the response to marketing campaigns 25 - 50% profit increase Assessing the impact of campaign decisions After marketers create campaigns, Predictive Marketing eliminates the guesswork of determining which ones to run. This helps marketers know in advance which campaigns are likely to be the most successful at reaching a specific goal, such as retaining at-risk customers or selling a particular product. It also shows which campaigns are not likely to be profitable.

By running only the campaigns that have the greatest potential for success, companies achieve

positive financial results. Monitoring and improving campaigns Feedback from campaigns enables the marketing department to measure the actual results of campaigns, as well as adjust in-progress campaigns when the initial results are not as positive as expected. Predictive Marketing stores all campaign interaction information, such as the offer made, the campaign used to make the offer, and the models used in the campaign.

This enables users to monitor: Campaign-level performance, such as actual response versus expected response, so users can see which segments and groups performed well Customer performance, such as customer profitability, cross-sell ratios, and attrition risk Channel performance, such as expected load on a channel versus planned load, and channel effectiveness for each campaign Predictive model performance, assess which models to continue to use and which to revise or refine.

Predictive Marketing uses data from recent campaigns to further refine its models. By tracking the performance of models and campaigns, companies create a "feedback loop" of information and refinement that enables them to create even more effective campaigns and achieve progressively better results. Integrating with social media Companies are making a transition from a method of listing to engaging in order to capture more value from social media.

Among the wide network of customers, predictive analysis helps business to plan it strategically to maximize the value of their social media interaction. Using techniques from data mining and text mining, predictive analytics lets you analyses at historical patterns and make predictions about future behavior for specific individuals. By taking customer data that you hold internally and adding what people have said and done, you can map out what people are likely to do and

engage them accordingly.

Enhance social media efforts with predictive analytics If you've got a social media game plan for monitoring feedback and engaging customers, consider adding predictive analytics to help you respond to customers in more proactive, targeted ways. As an example, by classifying sentiment (customer's opinion, comments, suggestions or thoughts about the product) in social media data and tying that to customer data, you can predict people who are likely to be favorable prospects with special messages or offers.

Here's one way you can get started: 1 . Capture 1,000 comments in the social media sites you monitor. You'll need to determine who to respond to, and how. 2. As its not feasible to respond to all comments, you can use text mining to classify sentiment, and based on the results; follow a 3-pronged response strategy: Send thank yoga's to positive comments - reinforce the relationship. Ignore comments with negative sentiment below a certain threshold - in some cases; it's more effective to focus on more receptive customers.

For those in between, send an invitation to engage via one-on-one social interaction with a support or sales representative. You can engage customers "in social" through outworks such as Twitter, Linked or direct them to your online email portal or phone bank. 3. Next, you'll want to measure the effectiveness of your response strategy. After planning your responses, test different messages (A/B testing) for each response type to gauge effectiveness, analyze and understand response rates, and refine your messaging. This testing will inform the engagement strategy you deploy going forward.

Adding predictive analytics to your social media efforts lets you capture more value sand ultimately, it

can help you gain a deeper understanding of your customers o more effectively engage them, increasing retention and loyalty A Microscopic and Telescopic View of Your Data Predictive analytics employs both a microscopic and telescopic view of data allowing organizations to see and analyze the minute details of a business, and to peer into the future. Traditional Bal was limited only to create assumptions and find statistical patterns to those assumptions.

Predictive analytics go beyond those assumptions to discover previously unknown data; it then looks for patterns and associations anywhere and everywhere between seemingly disparate information. Predictive Analytics-The Future Business Intelligence The market is witnessing an unprecedented shift in business intelligence (81), largely because of technological innovation and increasing business needs. The latest shift in the Bal market is the move from traditional analytics to predictive analytics. Although predictive analytics belongs to the Bal family, it is emerging as a distinct new software sector.

Analytical tools enable greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. However, past and present insight and trend information are not enough to be nominative in business. Business organizations need to know more about the future, and in particular, about future trends, patterns, and customer behavior in order to predictive analytics to forecast future trends in customer behavior, buying patterns, and who is coming into and leaving the market and why.

Traditional analytical tools claim to have a real 3600 view of the enterprise or business, but they analyze only historical data, data about what has already happened. Traditional analytics help gain insight for what was right and what went

wrong in decision-making. Today's tools merely provide rear view analysis. However, one cannot change the past, but one can prepare better for the future and decision makers want to see the predictable future, control it, and take actions today to attain tomorrow's goals.

Case study Let's use the example of a credit card company operating a customer loyalty program to describe the application of predictive analytics. Credit card companies try to retain their existing customers through loyalty programs. The challenge is predicting the loss of customer. In an ideal world, a company can look into the future and take appropriate action before customers switch to competitor companies. In this case, one can build a predictive model employing three predictors: frequency of use, personal financial situations, and lower annual percentage rate (PAR) offered by competitors.

The combination of these predictors creates a predictive model, which works to find patterns and associations. This predictive model can be applied to customers who are would be using their cards less frequently. Predictive analytics would classify these less frequent users differently than the regular users. It would then find the pattern of card usage for this group and predict a probable outcome. The predictive model could identify patterns between card usage; changes in one's personal financial situation; and the lower PAR offered by competitors.

In this situation, the predictive analytics model can help the company to identify who are those unsatisfied customers. As a result, companies can respond in a timely manner to keep those clients loyal by offering them attractive promotional services to sway them away from switching to a competitor. Predictive analytics could also help organizations, such as

government agencies, banks, immigration departments, video clubs etc. Achieve their business aims by using internal and external data.

Conclusion It was found that with the help of predictive analysis, organization were able to resolve one of greatest challenge faced in business organization (to find out the customer expectation, needs, key drivers of customer value and market segments) by way of analyzing transactional and other data to predict the likelihood that customer segments will respond to marketing messages. Predictive analytics enables marketers to understand the key factors that drive customer value and loyalty, and attract more customers.

This in turn supports organizations to improve customer segmentation, ensuring that the right customers get the right offer, and minimizing the perception of 'spam. ' It was also found that with the help of predictive analysis business organization were able to streamline the challenges faced in sales cycle and the sales process. Also it is being noted that predictive analysis support organization to have competitive edge with targeted approach and customized campaigns which brings in more business.