How Data Analytics Can Improve Your Mobile Trade-In Program

Posted by Kevin Kruse on Apr 17, 2019 9:00:00 AM

Data Analytics PhoneNow that equipment installment plans (EIPs) have become the predominant way for consumers to buy new mobile devices, consumers are holding on to their devices much longer.  As a result, analytics play an ever-increasing role in understanding how to entice trade-ins and price them when they come in to maximize the value of both the device and customer relationship.

A well-run trade-in program helps you offset the costs of customer acquisition. But if you want to get the most out of your program, you need a deep understanding of the secondary market and the different factors that impact trade-ins.

Trade-in Programs are a delicate balance of pricing, timing and promotion. With the deep insight gained through powerful data analytics, you can price your trade-in offers high enough to be enticing to the consumer but not too high that you can’t recoup your money (and then some) when the unit is resold into the secondary market.

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Understanding Trade-In Value (TIV)

Just as you wouldn’t price your new products in a vacuum without an understanding of the competitive landscape, you shouldn’t set the trade-in values for incoming devices without an understanding of external pricing structure.

The data needed to best set your TIV comes from a variety of sources - global carriers, on-line retailers and brick-and-mortar retailers, and smaller marketplaces. Once analyzed, this data can create a model catalog that maps phone models and conditions to make it simple and straightforward to set the most competitive, cost effective price for trade-in devices.

With the right tools, you can determine pricing rules relative to profit margin per unit or comparable competitive pricing. This empowers you to forecast profits from your program well in advance.

EIP and Early Upgrade Plan Pricing

While equipment installment plans (EIP) and early upgrade plans remain compelling to consumers, it can be difficult for carriers to confidently assess the financial risks associated with them. As the trends in device exchanges continue to adjust to consumer behaviors, carriers need to be able to predict future wholesale values to best price their offerings to drive new customer acquisition and sustain customer retention.

Data analytics can help mitigate the risk of the EIP landscape by providing predictive future wholesale pricing. By understanding the sifting risk exposure in each month of an EIP, carriers can best set the duration and the price of each plan.

Why Insight Matters

Incorrectly setting the trade-in price of a device by a few dollars may not seem like a big issue. But consider those few dollars multiplied by the number of devices traded-in over the course of the year - and you’re looking at potentially hundreds of millions of dollars of lost revenue. Those companies that are able to distill information out of the data available to them are best positioned to realize the most value from their trade-in program.

That’s where HYLA can help. HYLA Analytics provide maximum profit potential and mitigate risk associated with the buying, selling, pricing, processing and wholesaling of mobile devices.

If you want to gain an end to end view of mobile device value or better manage mobile device lifecycles, download this free white paper about reducing risk and increasing revenue through analytic insights.New call-to-action

Topics: Analytics

About This Blog

The HYLA Mobile blog is a place for thoughtful dialogue that will ultimately change the perception of “used” phones around the world. Visit the HYLA website to learn more.

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