Consumers want what they want when they want it. If you’re not there with relevant messages or offers at a consumer’s moment of truth, your competition may just be his or her next click.
In fact, high-impact recommendations are up to 50 times more likely to trigger a purchase than low-impact recommendations, McKinsey research finds. And contextually relevant messages result in six to seven times higher conversion rates than generic messaging – but 70 percent of brands fail to use them, according to Experian.
One significant reason: Nearly three quarters (74 percent) of brands cannot recognize their consumers in real time, an Acxiom study found.
Selecting the next best action for individual buyers at the key points in their journey demands robust data and analytics capabilities. Only then will marketers have the context they need to present the right message or offer at the moment of engagement. It’s that contextual relevance that makes all the difference in real-time customer engagement.
Consider: Interacting with consumers in real time is like adapting to the other person when having a face-to-face conversation. You need to interpret complex information in the moment, using what you already know about the person and new insights you learn during the interaction.
This may sound daunting or improbable for your organization – especially considering the myriad challenges marketers face as they move toward real-time customer engagement. The most significant barriers to real-time customer engagement are gaining insight quickly enough (40%), having enough data (39%), and inaccurate data (38%), according to a study from Forrester and Experian.
But real-time marketing doesn’t have to be as complex as it seems. Marketers can start their journey to robust real-time interactions by layering it into their existing marketing strategy, along with using some readily available tools and techniques that will help them overcome those three key barriers.
Most businesses have an overabundance of data, but all that information is stored in siloed systems. Marketers aiming for real-time customer engagement need connected data that will provide the context necessary to optimize customer outreach. And they require access to that connected data in real time so they can meet, and even surpass, customers’ expectations. Currently, just 24 percent of marketers use real-time customer activity to tailor their digital marketing, according to a Conversant study.
Most companies also need their many data siloes to support specific functional areas. This makes creating one massive store for customer data rarely the ideal solution. Instead, data-savvy companies use technologies that bridge those siloes to create a holistic view of the customer. These technologies pull data from relevant sources based on the specific information or insight marketers need, when they need it.
Real-time customer engagement is only possible when marketers have access to data that is complete, consistent, and current. Marketers must ensure that they have high-quality data by:
It’s essential to audit data sources on an ongoing basis to determine which have issues with quality, and then set the best courses of action to resolve those issues and eliminate bad data. Then, bridge silos using technologies that link multiple data sources and create a high-quality, holistic view of customer data by using customer identifiers, such as a unique customer ID or email address. Be sure to link online and offline customer data sources to build more complete customer profiles.
For mending faulty data, look for technologies that use approaches such as semantics and machine learning, which can help to improve the results of matching records and profiles. You should also look for open systems that integrate well with technologies the marketing organization is already using, or for platforms that have multiple capabilities necessary to ensure data quality. Don’t neglect the connectivity needed for that all-important holistic customer view either.
Finally, use data governance to set parameters for data types, fields, quality, usage, and “ownership,” as well as communicating who’s responsible for data governance. Delegate the responsibility and authority for data governance to the person or team with the most vested interest in ensuring data quality or the best understanding of how to maintain it. And make sure metrics and measurable goals are in place to monitor progress and avoid back-sliding quality.
Real-time personalization requires immediate access to relevant analytics. Machine learning provides it. In many cases, machine learning is already present all along the journey from basic to advanced contextually relevant real-time marketing. Marketers are using machine learning for customer segmentation, to learn how items such as behaviors and channels affect each other, and to uncover actionable patterns.
Leveraging machine learning accelerates this process because of its algorithmic and scalable approach to testing. By applying machine learning tools and techniques, we can more intelligently select messaging and channels appropriate to each customer, target, or prospect. This enables marketers to uncover the most impactful messages more quickly because they can automate delivery and rapidly deploy new strategies.
Contextually relevant interactions, provided at the moment of engagement, are the key to boosting customer satisfaction and maximizing revenue. Overcoming the three most substantial barriers to achieving real-time customer engagement – gaining insight fast enough, having enough data, inaccurate data – will enable marketers to reach their revenue and customer satisfaction goals. And that’s a scenario where everybody wins.