Marketers work constantly to try to stay ahead: ahead of the news, ahead of trends, ahead of customer expectations.

Spurred by an always-on culture, digital access, apps like TikTok, and lightning-fast internet connections, trends move quickly these days. Most marketing teams are probably familiar with the feeling of being caught off guard by a fast-rising trend in their market, or even within their own data — especially if that data shows something shocking like losing customers.

Artificial intelligence (AI) and machine learning (ML) can provide marketers an edge when it comes to getting ahead of the curve. While marketers will always win the day when it comes to creativity, algorithms are much better at analyzing data, especially huge amounts of data. With a great set of data, algorithms can spot opportunities (and warning signs) before the marketing team. By embracing AI and these insights, marketers can send more timely communications, offers and updates to help their customers. And, as we all know, personalized and timely offers and products lead to better conversion rates, customer loyalty and satisfaction.

1. Changes in Geographical and Temporal Buying Patterns

Trends often bubble up over a specific period of time — such as a few days or weeks — or within a geographic location. There is often a blend of temporal and geographic data, especially when trends start in one location and spread to other locations over time.

ML algorithms can spot these phenomena as they develop, so marketers can leap on the trend. Retail is one industry where this kind of AI-enabled trend-spotting is valuable.

For example, say that Kate Middleton wore a red skirt to a high-profile event. The outfit makes the news or is included in a popular blog. Suddenly, people in England begin to buy red skirts over the course of a few days or weeks. Perhaps the trend even crosses the pond, and people in the US Northeast begin buying red skirts.

ML-powered algorithms would spot this kind of temporal trend throughout its development, and tell marketers to send more communications or offers around red skirts.

Related Article: Marketing Experts Share Their 2021 Trends

2. The Impact (and Opportunity) of Upcoming Weather Events

Purchase decisions are often based on current events, especially changes in weather. Of course, brands already do seasonal marketing. When the winter months are coming, outdoor adventure retailers will naturally stock up on gloves, snowboards and skis.

However, ML can help brands get even more targeted. Rather than general expectations (winter is cold and often brings snow), ML makes it possible to find much more specific insights (a storm is developing over these three states and will be moving toward these other states next week).

By ingesting and studying weather patterns, algorithms can see a big snow storm on the way and advise marketers to start sending communications ahead of time. When ML and marketers look at a weather event as it moves across a region — say from the US Midwest into the Northeast — they can stay ahead of the trend. This adds value for customers, who will likely find shovels, salt and snow-sport gear sold out when the storm gets closer, and benefits the brand, which ultimately stands to sell more product and build customer loyalty.

Related Article: Universal Trends: Taking Triggered Marketing From Good to Great

Learning Opportunities

3. If Customers Are About to Jump Ship

Both of the examples above are predicated on data from real purchases. But what happens when there is an absence of data? In other words, what happens when people stop generating data that brands can use?

An absence of data is, in and of itself, data. ML algorithms excel at spotting these types of patterns. When combined with other data points — such as satisfaction surveys, calls to a service center, multiple returns, etc. — this kind of data provides strong warning signals that a customer is about to jump ship.

With this kind of insight, marketers can take a more nuanced approach to retention. Rather than sending another email blast or offer that exhausts the customer, marketers can segment their customers by their likelihood to respond. For example, if a customer is at risk of leaving the brand entirely due to a recent inconvenience, marketers could send a generous coupon or offer to woo them back.

Machine Learning for Better Customer Communications

All the examples above bubble up to a core tenet of modern marketing: have empathy for your customer. By understanding the trends that drive them, the current events that impact them, and their experience with your brand, marketers can deliver empathic and meaningful communications at scale.

Of course, this kind of deep analysis requires really great data. Third-party data can play a helpful role, though the landscape is rapidly changing with Google’s shift away from traditional cookies. First-party data is often the best, as customers have consented to share the data with an organization and have indicated willingness to be contacted.

Whatever the blend, organizations should ensure they have a rich set of data for ML algorithms to analyze and understand. And that dataset should be updated, especially in cases where current events or weather play a role.

Machines and humans can make for a powerful marketing combination. Make sure you’re partnering with your data to experience the fullest benefit.

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