Mid-year checkpoint: Three data-dependent marketing strategies

The start of the second half of the fiscal is a natural point for businesses to look back and reevaluate.

In any year, marketers might look back at goals set in January and compare actuals to projections. But due to current events, many will find that mid-year 2020 is anything but ‘as planned’ on several levels.

A mid-year evaluation on what worked and what didn’t in this stifled operating environment will be key – especially across digital channels which were the main mode of sales in most regions for many months.

Did you effectively reach and engage customers? Is there any patterns on cart abandonment? What can you learn from these findings that will inform a stronger rebound in the second half?

Whether you’re prioritizing an individualization strategy for your existing base or seeking new prospects in the back half of the year, contextual intelligence can help drive real value.

Validating and enriching your brand’s own customer data with comprehensive third-party intelligence can provide a deeper, more holistic understanding of existing and prospective customers.

Knowing more about preferences, (Read more...) habits, and behaviors outside of known brand interactions can help marketers hone their new acquisition messaging and tactics.

Not sure how to get started? Consider how contextual intelligence can help you achieve your goals in the second half and beyond:

1) Grow your data-base

Aside from keeping existing customers satisfied, the number one objective of almost every business is to grow its base. This goal isn’t always easy to achieve; it involves uncovering—and then reaching—potential new audiences.

The most reliable way to do this is through an in-depth analysis of existing, current customer profiles, before seeking out new pockets of consumers with similar characteristics.

A robust and ever-evolving dataset can help marketers do both.

First, businesses can drill down into the data they hold about their best, most loyal customers to get a better understanding of who they are, including specific habits and attributes, and where they ‘live’ in the ecosystem.

Second, businesses can compare this more articulated customer model against vast volumes of third-party consumer data to add contextual intelligence and identify prospective customers with greater precision.

It’s about more than just data; instead, it’s about understanding correlative consumer behaviors.

For example, using where consumers live, what they like, how many seconds they spend looking at a particular type of advertising or online platform, and then using this intelligence to find similar profiles with overlapping characteristics is ideal for brand advertising.

It’s a two-step process of constantly enriching owned data to build a more three-dimensional customer profile, and then using that to make better-quality choices about customer acquisition. And ultimately, it’s about getting the right consumers in front of the proper marketing on the right platform.

2) Understand customer 4.0

You might think you know your customer well, but really, you’re just dealing with a fragment of their broader existence. Sometimes, what you don’t know about them will mean that your marketing efforts miss the mark.

These days, shoppers are inundated with ads competing for their attention, and increasingly consumers have a low tolerance for poorly judged or irrelevant content. Brendan Witcher, a principal analyst at Forrester, illustrates this well:

“If you send out an email to 10 million people, and last year, you got 1,000 sales on it, this year you got 1,500 sales, everyone’s high fiving like, ‘Woohoo, 50% lift in sales!’ Except you were irrelevant to 9,998,500 people. You do that 136 times a year, three emails a week, how healthy do you think your customer file is going to be?”

Individualization is key, fueled by third-party data that fills in the gaps. The fewer misfires a brand has, the more likely they are to entice new customers and to maintain the faith of their existing fan base.

This strategy of intention is critical since only 20 percent of global shoppers think the offers they get from retailers are always relevant or personalized, according to Oracle Retail Annual Consumer Research Report.

Third-party customer data, sourced from a range of applications, call centers, and social platforms, can add new points of reference to customer profiles, helping to shape and hone a keener, more accurate impression that can inform better engagement.

Knowing when transactions happen, how shoppers react to specific interfaces, and how seasonality affects behaviors can all be critical to boosting sales.

Beyond ads, a more personal, profitable relationship with existing customers can be cultivated with a better-quality evaluation of the data accrued from loyalty programs. It’s about delivering exactly what people want and when they want it.

3) Use data to make smarter business decisions

Effective marketing is reliant upon reliable business decisions, and better-informed customer insights can play an essential catalytic role here too.

From optimizing assortments to site planning, the entire enterprise benefits from the more complete picture of the customer and broadens perspective.

Ensuring that owned insight is combined with the right external cues to enrich it—whether that’s customer location data gleaned from web order shipments or generalized trends and buying behaviors—provides a sound formula across the board.

Indeed, any retail decision, from the assortment offered to promotions served up at checkout, can stand to be bolstered by a marketer’s data insight. When this data is consistently refreshed and reassessed, it can deliver precise, up-to-date intelligence to the business.

By layering artificial intelligence and machine learning into your tech stack, you can glean feedback on how effective new data insights are in practice, allowing you to swiftly update and adapt your retail approach to optimize its marketing draw.

Data is no replacement for human experience or intuition. A trifecta of descriptive, prescriptive and predictive analytics can be a competitive advantage when it comes to making targeted decisions that drive growth.

Reinforcing owned data with insight from the broader environment will bring a brand new dimension that can bolster existing data-driven retail strategies, ultimately supercharging success in the second half of the year and beyond.