Data Analytics Is Better, Easier, and More Critical Than Ever: Successful companies have made data analytics a part of their DNA for a reason.
Let's examine how three global companies put data analytics to work:
CASE STUDY NO. 1: AUTOMOTIVE SALES & MARKETING EFFECTIVENESS
Every automotive company worldwide is challenged by how best to spend its sales and marketing dollars, and how to determine the impact of this spending. Using standard tools found in most data analytics software packages, Automotive Company X examined baseline sales trends, analyzed repeat buying trends and model preferences, did cross-brand purchase analysis including model preferences, built repeat-buyer look-alike models, and performed campaign-conversion predictive modeling. The output of this analysis allowed the company to know which existing and competitive customers to target for its cars, which car model to promote to each customer, which channels to use to reach each customer, what contextually relevant messages will best resonate with each customer, and the impact of each marketing dollar spent, which was accomplished using closed-loop measurement tools. Its data analytics program was able to drive dramatically higher car sales.
CASE STUDY NO. 2: SELECTION OF PARTNERS FOR A NEW FINANCIAL PRODUCT
Financial Company Y wanted to launch a new line of mutual funds to complement its existing annuity products. It sells its products through more than 30,000 independent financial advisers. The challenge: determining which financial advisers would be best to work with to launch the new product line, and why. To identify the advisers with the highest probability of cross-selling mutual funds and generating maximum sales, Financial Company Y created two analytical models. One model provided a predictive score to suggest which high-potential advisers would be good candidates to cross-sell mutual funds. The second model predicted total mutual fund sales for each high-potential adviser. The result of this data analytics exercise was impressive. The company was able to select the right group of financial advisers; the launch of its new mutual fund product line was completed in record time; and it produced strong financial results.
CASE STUDY NO. 3: CONSUMER SEGMENTATION & CUSTOMER JOURNEY MAPPING
Consumer Company Z offered a program where it trained people who sign up, pay a membership fee, and get certified. It had two challenges: growing sales and addressing the high level of churn among its members. To overcome these challenges, Consumer Company Z concluded it needed better segmentation with personas and a deeper understanding of its customer journeys by segment. The goal of a journey map is to focus resources on the touchpoints having the biggest impact on satisfaction across the customer life cycle, including both "moments of truth" and risk factors. Using data modeling, the company integrated customer profile and transactional data with third-party lifestyle data to build a predictive model that included a scoring algorithm to guide appropriate activities for its new segments. The insights and results to date have been amazing.
In all three examples, the initial focus was on securing data of high quality and integrity, which is a prerequisite for a successful data analytics program. Data needs to be accurate, complete, reliable, accessible, and timely. This is not always easy to accomplish. The diagram shows what a well-constructed data analytics program looks like from start to finish.
CLOSED-LOOP DATA ANALYTICS
To have meaningful business impact, a data analytics program must also be a closed-loop program. Campaigns must be measured, and continuous improvement must take place in support of established business goals and strategy. Below are some examples:
* Business goal: Improving lead generation. Measurements: The number of leads received, the number of leads qualified, the close rate, and the cost per lead.
* Business goal: Building awareness via email campaigns. Measurements: The number of opens, the click-through rate, and the number of conversions for each call to action.
* Business goal: Gaining insight using identity resolution. Measurements: Securing business by approaching competitors' customers, customer retention, and geo-targeting; lease renewal; recall notification;
recall challenge; and attribution of offline behavior to online advertising. * Business goal: Achieving peer-to-peer exchange via social communities. Measurements: Engagement via pages views, posts, and polls.
Closed-loop data analytics are achieved with products like Identity Link from LiveRamp, which ties together CRM data, transactions, cookies, and device IDs into a single customer profile. This allows the company to accurately attribute sales back to both specific tactics and media (mobile, TV, display, etc.). To learn which tactics and strategies are best to drive offline transactions and find out how to invest marketing dollars in the future, companies are also increasingly enlisting help from measurement companies like Marketing Evolution.
If your goal is to thrive in today's digital deluge, now more than ever is the time to make data analytics part of your company's DNA.
BY BARTON GOLDENBERG
Barton Goldenberg (firstname.lastname@example.org) is president of ISM (www.ismguide.com). Since 1985, ISM has established itself as the premiere strategic advisor to organizations planning or implementing 'Customer-Centric' strategies to address Data Analytics, Digital Transformation, CRM, Social Media Communities, CustomerEngagement, and Emerging Technologies initiatives. He is a frequent keynote speaker (www.bartongoldenberg.com) and is the author of four books including his latest, The Definitive Guide to Social CRM. He is currently completing his new book, titled Engaged Customer Strategy: Your Roadmap to Success in 2030.
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|Title Annotation:||CUSTOMER EXPERIENCE|
|Date:||Dec 1, 2018|
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