The Impact of Big Data Predictive Analytics on Customer Relationship Management Practices - Case Study Example

The Impact of Big Data Predictive Analytics on Relationship Management Practices The Impact of Big Data Predictive Analytics on Customer Relationship Management Practices 1. Introduction Predictive analytics within different business contexts is a notch higher following the advent of big data. To establish the impacts of the concept on Customer Relationship Management (CRM), an exploratory research study that employed mixed-methods approach of in-depth interviews and case studies was performed. Interview data was collected from the field experts whereas case study data was obtained from secondary sources. The case studies were subjected through within-case analysis plus cross-case analysis. On the other hand, the interview data underwent the thematical analysis using NVivo. 2. Study Population In view of the limited number of the cases studies, no restriction was imposed on the location of the company under study. The companies chosen were those that operate in B2B and B2C markets. Nevertheless, the company used as a case had to employ predictive analytics. Non-probability sampling was employed during the selection of the case studies in order to create a match with the research objectives. A search on "big data" and predictive analytics was conducted on the IBM and Case Centre websites. The search process focused on cases that discussed the utilization of predictive analytics and the big data concept in the context of marketing. The selected cases were: Walmart, Amazon, Trident Marketing, Fiserv, and Netflix. The case studies were analyzed using within-case analysis and cross-case analysis. Data from the semi-structured interviews was analyzed using NVivo qualitative analysis software through the use of an inductive coding method. The research used non-probability sampling. Additionally, the nature of the research study dictated that the researcher surveys experts in CRM and predictive analytics. The experts were provided by the network of supervisor as well as the researchers social media channels. Semi-structured interviews were used to interview the experts. 3. Key Findings The key findings of this research are: Legacy systems, dynamic capabilities, as well as benefits realization, are some of the problems that surround the implementation of big data solutions within different organizational contexts. These problems made the flexible digitally native companies be in a position to compete with the bigger companies that featured the analytical capabilities. The concept of big data predictive analytics helps in informing the strategic decision-making processes in organizations alongside allowing the organizations to have more efficient market automation. The three forms of CRM (strategic, operational and analytical) are becoming closely related. 4. Who Uses Predictive Analytics? 4.1 Walmart Actions Walmart operates within a B2C market context within the retail industry. The company collaborated with a third party in its bid to develop a Customer Advantage program that offers insight into issues like what population purchases a particular brand, and what brand is more efficient in given geographical regions. Walmart leveraged big data solutions in order to process large data sets from different sources. The company used numerous source of information like transactions alongside shared data from partners plus retailers. Additionally, the company has acquired Kosmix, which provides analytical services on different social media platforms. This move has helped Walmart to gain additional data sources like discussion boards, blogs as well as pictures. Effects The insights from the adopted solutions helped the company in a number of spheres like manufacturing, product development, procurement, marketing, distribution, operations, sales, merchandising plus human resources. In particular, Walmart was in a position to establish the marketing campaigns or promotions that would most effectively helping in spearheading customer engagement, traffic and sales basing on the customer attributes plus behaviors that were obtained using the implemented solutions. The information enabled the company to optimize the prices associated with the perishable goods. Consequently, Walmart was in a position to forecast the products with a higher likelihood of selling in particular stores. This brought about improved inventory and supply chain management, more so during the seasonal periods. As of 2013, the big data concept had allowed Walmart to attain a competitive edge, with the suppliers attaining a 5% increase in the web-based sales along with incremental revenue tuning to 1 billion USD (Romanov 2013). 4.2 Amazon Action Amazon operates in a B2C context within the online retail industry. Earlier Amazon depended on the analytics staff to manually assign book recommendations in a bid to match the customer to other similar customers. Later on, the IT department implemented solutions automated this process whilst expanding it to varied products in addition to the books. The companys big data system was capable of issuing real-time analytics regarding customer recommendations. The new method offers real-time personalized recommendations and can supply quick responses to any changes like new ratings or purchases. At present, Amazon is on the move to leverage the information collected in the past years to create 360 degrees customer profiles that can track and store every piece of customer information like purchase history, browsing history and social data among other details. Effects Big data analytics made Amazon be in a position to adventure into new markets like the movie streaming service that has helped to improve their personalized marketing. Through the use of profile information pertaining to customers, Amazon was in a position to recognize the set of customers that it could target using particular offers. Besides, they were in a position to tailor product recommendations to match given customer groups instead of the mass recommendation offers. Amazons IT utility also employs Robo-Pricing to scan for prices offered by competitors. Through that information, Amazons system can manipulate prices on the companys site whilst guaranteeing that Amazon has better prices to offer in comparison with what the competitors have in store. Putting a new system in place allowed Amazon to quickly retrieve data. Besides, the data structure permitted the staff to access quickly the companys databases thus permitting well-timed responses to complaints and concerns from customers. Consequently, a significant improvement has been seen in the level of post sales services. As of 2014, Amazon-owned financial data revealed a net loss tuning to $39 million plus a net income tuning to $274 million (NASDAQ, 2014). 4.3 Trident Marketing Actions Trident Marketing operates in the B2B market context within the marketing services industry. Trident Marketing implemented a predictive analytics system that allows it to constantly add data after every 15 minutes. Effects The new solution helped the company in a number of ways. First, it was capable of managing customer churn in a more effective way. The analytics can forecast the clients propensity to terminate their dealings with Trident Marketing within a span of 12 months. Heightened churn rates provide Trident Marketing with an opportunity to pay more attention to other aspects like service quality and sales calls. Second, the concept helps the telemarketers to locate the appropriate customers to make phone calls to, and in what geographical region plus a decision of the products that the customers are to be offered with. Use of predictive analytics in this context has helped Trident Marketing forecast on the sales person who would be better positioned to hit a given sales target, and what product would best fit such an endeavor. Trident Marketing was in a position to establish the particular Internet keywords that produce the greatest volume and the hugest profit per click in the pay-per-click adverts. As of 2012, Trident Marketing saw sales level heightening by 10% and the marketing costs declining by 30% within the initial 60 days of implementation of the predictive analytics tools (IBM, 2012). 4.4 Fiserv Action Fiserv operates in the B2B market context within the data analytics industry. Fiserv introduced meaningful IT infrastructure that was aimed at providing additional services to customers. This new solution brought together numerous transactions from areas such as banking account processing, person-to-person payments, mobile payments, e-bill payments, electronic funding transfer whilst using predictive analytics to establish the most probable needs of customers at some later time. The predictive analytics tool sought for the present and the changing expenditure patterns among clients in order to adopt better approaches to target customers. The insights were then fed in business intelligence dashboard which was then availed to staff members to inform their decision-making process (IBM, 2012). Effect The models permitted the sales personnel to focus effectively on customers with a higher likelihood of making purchases. Besides, the models offered insights into issues like the appropriate price ranges to offer and the risk factor that pertains to every client like customer attrition. The effects of the analytics aided in Fiservs desire to improve the response rate regarding all marketing initiatives that targeted their customers. Additionally, it witnessed its marketing cost reducing by 50%. 4.5 Netflix Action Netflix operates in B2C marketing context in the online streaming industry. Netflix collaborated with Amazon in order to implement a cloud-based data infrastructure following the growth in the number of subscribers to 50 million that its legacy systems became incapacitated to handle. It gathered 50 individualized files on every viewable item including fast forwards, pauses, rewinds, pause, ratings plus the device that was employed to view, bookmark or categorize the preferences of customers. The viewing patterns were then passed through complicated algorithms in order to bring about a clearer understanding of the viewing habits and individual preferences of customers. Effect The analytics-generated insights permitted Netflix to produce precise recommendations for different viewers. Basing on the actions and preferences that are particular to a given viewer, the algorithm employed is capable of predicting the subsequent TV show or movie that the customer in question is likely to enjoy. This has brought about heightened customization of the Netflix-offered services. Every customer is in a position to view an entirely customized home page whose contents reflect the individual preferences of that given customer. Insights from this substantial data permitted Netflix to create content that is unique to it. Basing on the individual preference associated with the viewer, the company was in a position to recognize the appropriate balance of actors plus movie genre in order to restructure the show House of Cards. Besides, the company used the preferences of the viewer to customize the trailers associated with the show. If, for instance, a viewers history constituted action movies, the reader would be made to go through the race energetic version. The show was a great success receiving up to 10,000 comments on social media on the first day of release. 5. What Do the Experts Have to Say? Theme 1: Big Data Analytics Node 1: Importance of Big Data Analytics Imperative Experts underscore the growing number of information sources that are within the reach of organizations. The growth increases the amount of analytics that different organizations run. Any expenditure channeled towards analytics tools is deemed as an appropriate choice that can go a long way adding value to a business enterprise. In future, investment in analytics will not be an option, but a necessity for any company that wishes to survive in the marketing arena. So, experts echo the message that spending in the analytics solutions is still a worthwhile step that companies should treat as a matter of urgency. Effective Expenditure A number of experts subscribe to the idea that operational cost concerns are the main source of motivation for companies contemplating using big data predictive analytics. In the past, firms have depended on intuition to make marketing decision, an approach that could be too expensive to the business in case things do not turn out as expected. Node 2: Implementation issues A Focus on Business Benefit Implementing the predictive analytics tools, in itself, does not introduce any benefits to the company. The analytics solution must be aligned to a business objective that an enterprise aims to achieve. On the same note, organizations have to understand what an analytics solution is up to in a business context in order to realize higher ROI. People: Dynamic Capabilities Introduction of analytics solutions should be coupled with personnel with an in-depth understanding of data. Though the process is time-consuming and expensive, development of people capability is crucial if a company is to reap any value out of an investment in the predictive analytics solutions. People: Uncomfortable Managers Advanced capabilities in the big data solutions inform a number of the decision-making processes, a factor that breeds discomfort among managers. Consequently, many managers fail to appreciate automation while wishing to make decisions of their own. Legacy Systems Legacy systems are great barriers to implementation of big data predictive analytics tools. Often, replacement of the legacy systems prove too costly to pursue making some companies to retain the legacy systems. Node 3: Opportunities Digitally Native Companies Whereas this problem faces a number of the large companies, the digitally native organizations have an easier time implementing big data predictive analytics solutions because of reduced costs. The organizations feature improved analytics capabilities that have a better integration into the marketing functions thus allowing them to do things more efficiently. Theme 2: Predictive Analytics Uses Node 1: Customer Acquisition Channel Management The customer interacts with a host of channels prior to making purchases. An attempt to manage these channels with the same level of resources is expensive and inefficient. Going by the suggestions of the experts, predictive insights are critical in recognizing every market segment alongside the channel that a given segment is likely to interact with. Displaying appropriate content in the relevant channels could drive customers up the sales funnel in addition to the provision of greater attention to useful channels. This method is more effective if an organization is contemplating getting rid of uncalled for marketing costs. Node 2: Customer Retention Customer Loyalty Other than its ability to administer customer preferences, predictive modeling can recognize fellows who are likely to be loyal as was the case in Air Canada. This airline company uses the airline services to target customers then offers them an invitation to form part of a loyalty program where they can receive appealing offers or benefits. Customer Churn Experts assert that predictive analytics allows organizations to recognize then manage the attrition of customers. Telco, for instance, uses analytics tools to predict if they are on their way to defect. It can then leverage this information to prevent such misfortunes from happening. Air Canada uses predictive analytics in the selfsame manner. Node3: Personalized Marketing Cross/Up Selling Recognizing specific patterns in the purchase trends can reveal given customer attributes that allow organizations to decide if customers could accept given offers. Air Canada, for instance, establishes customers who are not very cautious about prices then sells them business class upgrade. Product Recommendation Predictive analytics can help in recommending different products for customers. Dell, for instances, uses predictive modeling that is capable of recommending the products that need to be sold via the call center and online channels. 6. How Has the Concept of Predictive Analytics Changed CRM Processes? Decision makers and managers in other business contexts have not had a proper perception of predictive analytics and marketing automation in spite of the host of benefits that the concept brings about. Their reason for opposing the system is mainly based on the idea of a machine making a decision in place of a human being. Continued use of predictive analytics is particular feared to render positions like that of a CRM manager obsolete given that more and more functions are becoming automated. This gives the meaning that CRM positions will require individuals with stronger analytical skills alongside proficiency in handling different data formats. Findings in this research study hint that the market is taking a different dimension and will soon be science-oriented. Great transitions are already evident in analytical, operational and strategic CRM. Cases of Netflix and Amazon are good illustrations of how predictive analytics has transformed operational CRM. Cases of Walmart and Air Canada are proper illustrations of how predictive analytics has transformed analytical CRM. Whereas CRM occurs in the three forms (analytical, operational and strategic), the lines distinguishing these three forms are gradually becoming blurred. The three forms are beginning to integrate into something that looks more of an individual customer centric strategy that is underlain by predictive analytics. Soon, the presence of proper analytics could be the only indication of a business that is proximate to attaining a competitive edge. Predictive analytics tools featuring more enhanced algorithms plus data sets are likely to feature better marketing automation. With the existence real-time predictive analytics, insights originated from analytical CRM revolving around customer acquisition and customer churn are bringing about new effects in strategic CRM. Prior knowledge about a customers intended defection creates room for appropriate and timely defensive measures. On the same note, knowledge about variables that dictate customer loyalty allows for decisions that are tuned towards realizing loyalty. Conclusion Marketing automation and big data introduce more dynamic approaches to customer segmentation in the contemporary business world. This is attributed to the growing channels that customers interact with, and the meaningful data that is generated in real-time. Organizations wishing to support prompt decision making and marketing automation could follow the examples of companies like Air Canada, Trident Marketing and Fiserv that have used real-time predictive analytics to take their CRM services a notch higher. There is an extra value when companies decide to automate their services; there is a significant reduction in the operational costs and improved responsiveness to the concerns raised by customers. It is on this ground that the automation is already finding its roots in a number of the business contexts. Bibliography IBM. Fiserv (2012). Big data and Analytics. Retrieved from http://www.ibm.com/big data/us/en/big-data-and-analytics/case-studies.html Romanov, A. (2013). Putting a dollar value on big data insights. Retrieved December 28, 2014, from http://www.wired.com/2013/07/putting-a-dollar-value-on-big-data-insights/ NASDAQ. (2014). AMZN Company Financials. Retrieved December 28, 2014, from http://www.nasdaq.com/symbol/amzn/financials?query=income-statement Read More