AUTHORS

Brandon_Mills-10924_headshot_small
Brandon Mills
Manager,
ZS Associates
Jason_Bell_11099_headshot.jpg
Jason Bell
Associate Principal,
ZS Associates
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John DeSarbo
Principal,
ZS Associates
Kyle_Heller_thumbnail-1
Kyle Heller
Associate Principal,
ZS Associates

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Predicting Customer Behavior Is Even More Critical With Flexible Consumption Models

Posted by Pramil Jain on Mon, Aug 13, 2018

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This post is the third in a six-part series on strategies for high-tech companies and business leaders to adopt to meet the evolving requirements associated with flexible consumption models.

From telemetry to the internet of things, today’s organizations have access to unprecedented amounts of sales, marketing, and product data. When harnessed correctly, this data can help salespeople make decisions throughout the sales cycle. With the new model of flexible consumption, it’s more important than ever to take an “always on” approach to customer insights, having your finger on the pulse of what customers need at all times. It’s also more critical to predict customer needs and actions, since customers are now more likely to churn or turn to your competitors for additional services.

Data can help you identify growth opportunities by directing you to customers who are researching products and might be ready to engage with your firm. For example, compared to your competitors, are customers using your product at the same frequency, and are they using all the features? Knowing this information will not only help sales to drive growth, identify at-risk accounts, improve the relationship and drive upsell/cross-sell but would also help product and marketing teams to improve their product and messaging respectively.

Here are four tips for tech companies looking to make the most of their data to better support a flexible consumption model:

1. Get the right people. With flexible consumption models and multiple providers, it’s easier for customers to try your product or service, then cancel it. To get ahead of your competitors and have a better customer retention, it’s critical to not only understand but also predict customers’ needs or behaviors. Predicting is an art which requires both a good understanding of your customers’ businesses and detailed knowledge of all the latest machine learning and AI-related techniques. When it comes to maximizing potential insights from customer data, one of the key roadblocks that most companies face is the lack of data specialists who are skilled at this. Firms should hire or internally develop personnel with data management experience. If you hire a data scientist, make sure that person can also identify and understand your clients’ business problems, and then create models and algorithms for them.

2. Collect detailed, end-to-end data on customer usage to power predictive analytics. AI-based models go a long way in helping companies score leads based on hundreds of features that would be humanly impossible, such as contact information, data on the firm, historical behavior of the lead and your marketing interactions with them, and determine the best method and time to contact each lead. AI also tracks customer usage behavior to predict when it’s time to renew or upsell. AI-based chat boxes can send automated emails or notifications to salespeople based on customer usage and activity, which is valuable in the new flexible consumption model environment, where customers buy services a la carte rather than getting locked into long-term subscriptions.

3. Evaluate current customers for their probability to churn or upsell. In a flexible consumption model, the salesperson role doesn’t end after selling the product or service. You need to make sure that customers are actually adopting your technology, as low or no adoption could lead to customer churn. You should constantly track customer activity and have a model in place to flag churn or upsell-related cases. The ideal model should take data inputs from general firm information, upstream information (marketing campaigns, sales calls and emails) and downstream information (historical revenue, win rate, and upsell and churn triggers). This can help you not only take action to re-engage a particular customer, but also serve as a case study for others.

4. Quantify and communicate value-add through insights that are easily accessible and relevant. As companies’ business development managers are mostly only involved in purchasing decisions and not implementation of products, it’s critical for salespeople to share regular reports with them to show the value-add or ROI of their products. This will not just help in reviewing the contract, but it’s also an excellent opportunity to build relationships with business development managers.

While the amount of data available can seem overwhelming, with the right processes and personnel in place, tech firms can use data to predict customer behavior and prevent customer loss in the new world of flexible consumption.


RELATED CONTENT

BLOG POST: Five Strategies for Tackling Flexible Consumption Models

BLOG POST: Why Flexible Consumption Models Require an 'Always On' Approach to Customer Insights


 

Topics: flexible consumption, data and analytics, customer insights, customer behavior, High Tech, buyer behavior

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AUTHORS
Brandon_Mills-10924_headshot_small
Brandon Mills
Manager,
ZS Associates
Jason_Bell_11099_headshot.jpg
Jason Bell
Associate Principal,
ZS Associates
John_DeSarbo_thumbnail
John DeSarbo
Principal,
ZS Associates
Kyle_Heller_thumbnail-1
Kyle Heller
Associate Principal,
ZS Associates
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