MKT 711 Week 3 Technology as a Marketing Enabler Example

Alerts, Dashboards and a CRM Nobody Opens: Technology as a Marketing Enabler in a Dairy Sensor Firm's Customer Relationships

[Student Name]

University of Phoenix

MKT/711: Marketing and Managing the Customer Relationship

Week 3 Assignment

[Instructor Name]

[Date]

HerdSense Technologies, its systems and its data are composites written for a model paper.

HerdSense Technologies, the composite dairy sensor company from Madison, Wisconsin, studied in this course, gathers rumination, activity and temperature readings from about 410,000 cows every few minutes. Its software turns those readings into alerts that help farmers find sick cows and cows in heat. Week 1 found that relationships are weakest with large direct accounts, and Week 2 showed that raising large-herd retention by even a few points would add substantial customer equity. Yet when an account manager wants to know whether a large customer is at risk, she must look in three systems and often relies on memory. This paper evaluates technology as an enabler of HerdSense's marketing and customer relationships and recommends how to use it better.

What CRM Is

Payne and Frow (2005) proposed a strategic framework that defines customer relationship management as a cross-functional process for achieving continuing dialogue with customers across all contact points, with personalized treatment of the most valuable customers. Their framework links five processes: strategy development, value creation, multichannel integration, information management and performance assessment. Technology supports information management, but CRM succeeds only when all five processes connect. The definition moves attention away from software and toward how the firm creates value with customers.

Measuring the CRM Process

Reinartz et al. (2004) developed measures of the CRM process in three stages, relationship initiation, maintenance and termination, and surveyed firms in several countries. They found that implementing CRM processes, especially in the maintenance stage, tracked with stronger results for the firm, while CRM technology by itself contributed relatively little. The maintenance stage, which includes retention efforts and cross-selling, is where HerdSense's churn problem sits.

How CRM Raises Satisfaction

Mithas et al. (2005) examined data from hundreds of companies and reported that the use of CRM applications was associated with higher customer satisfaction, and that the effect worked largely through better customer knowledge. CRM applications helped firms learn about customers and use that knowledge to tailor service. Supply chain integration strengthened the effect. The mechanism, knowledge of the customer, suggests that HerdSense should judge any technology by whether it improves what staff know about each farm.

Pitfalls

Boulding et al. (2005) reviewed CRM research and identified pitfalls, including viewing CRM as a narrow technology initiative, focusing on the firm's interests rather than customer value, failing to integrate CRM across the organization and underestimating the difficulty of acting on customer data. They argued that CRM works when firms integrate value creation for both customer and firm and when data are used to change behavior.

Auditing HerdSense's Systems

HerdSense has three main systems. The herd platform holds all cow data and alerts and records when and how often each farm opens the dashboard. The CRM system, bought three years ago, holds contacts, deals and contract dates and is used mainly by sales. The support ticketing system records calls and problems. None is connected to the others. Support staff seldom open the CRM, and sales staff never see support tickets.

What the Data Already Reveal

An analysis of last year's 62 large-herd cancellations, linking the three systems by hand, found that in the six months before canceling, 48 of those farms had opened the dashboard less than once a week, compared with daily use among renewing farms. Thirty-nine had logged repeated tickets about integration with their farm management software. Only 11 had had any contact with a HerdSense dairy scientist. The warning signs existed in the company's own data, but no one saw them together.

HerdSense knows more about its customers' cows than about its customers.

The Customer's View of Technology

Farmers experience HerdSense's technology mainly through alerts on their phones. Interviews with large-herd managers revealed a common complaint: too many alerts, many of them false alarms, during busy seasons. Several said they had turned off notifications and checked the dashboard only when they had time, which explains the falling use before cancellations. Technology that overwhelms customers can damage the relationship it was meant to support. Any CRM improvement must therefore include tuning alerts to each farm's preferences and herd size, so that the product itself does not push customers away.

Dealers in the System

Dealers sit outside all three systems. They learn about problems when farmers call them, often before HerdSense does, but have no way to log issues or see a farm's alert history. Giving dealers limited access to account health information, with farmers' consent, would extend the CRM process to the partners who carry much of the relationship.

Mapping the Audit to the Framework

In Payne and Frow's terms, HerdSense's strategy development has not decided which customers deserve personalized treatment, though Week 2 now supplies that answer. Value creation is strong in the product but weak in service to large accounts. Multichannel integration is poor, since dealers, sales, support and scientists act separately. Information management is fragmented. Performance assessment tracks sales but not retention by segment.

Recommendation 1: Account Health Scores

HerdSense should link the three systems so that each account receives a monthly health score combining dashboard use, alert response rates, open support tickets and time since last expert contact. Scores would appear in the CRM for every account manager and support agent. This applies the knowledge mechanism Mithas et al. (2005) identified.

Recommendation 2: A Renewal Playbook

When a large account's score falls below a threshold, a playbook would trigger a review: a call from the account manager, a herd results review with a dairy scientist and priority help with integration. This strengthens the maintenance stage that Reinartz et al. (2004) linked to performance.

Recommendation 3: Traditional Tools Alongside Technology

Technology cannot replace the expertise that Week 1 identified as the strongest relationship driver. Quarterly in-person herd reviews for large accounts and an annual dealer and veterinarian conference remain essential. The technology's role is to tell people where to spend their time.

Adoption and Governance

To avoid the pitfalls Boulding et al. (2005) describe, the project will be led jointly by the heads of customer success and sales, not by information technology. Support and account staff will be trained, and retention, not data entry, will be part of their goals. Farm data used in health scores will be limited to usage patterns, not herd performance, without each farm's consent, and the data policy will state this.

Measures

Success will be judged by large-herd retention, the share of at-risk accounts that receive a review within two weeks and changes in dashboard use after reviews. A staged rollout, beginning with half of large accounts chosen at random, will allow comparison.

Limitations and Research Opportunities

The cancellation analysis is retrospective and small. A staged rollout offers a chance to test causal effects of health score interventions, which would add to the limited experimental evidence on CRM in business-to-business subscriptions.

Conclusion

Research shows that CRM is a strategy and process in which technology plays a supporting role, that maintenance processes and customer knowledge drive results and that treating CRM as software invites failure. HerdSense's data already contain early signals of churn, but disconnected systems hide them. Linking those systems into account health scores, acting on them through a renewal playbook and keeping expert relationships at the center would turn technology into a true marketing enabler.

References

Boulding, W., Staelin, R., Ehret, M., & Johnston, W. J. (2005). A customer relationship management roadmap: What is known, potential pitfalls, and where to go. Journal of Marketing, 69(4), 155-166. https://doi.org/10.1509/jmkg.2005.69.4.155

Mithas, S., Krishnan, M. S., & Fornell, C. (2005). Why do customer relationship management applications affect customer satisfaction? Journal of Marketing, 69(4), 201-209. https://doi.org/10.1509/jmkg.2005.69.4.201

Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167-176. https://doi.org/10.1509/jmkg.2005.69.4.167

Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293-305. https://doi.org/10.1509/jmkr.41.3.293.35991