on Apr 29, 2022 / by Abbi Tanton

Stop Being Reactive on Social Media—Get Predictive

The importance of social media in B2B marketing is no longer up for debate, and any business worth its salt should have presence on at least one social platform. Given its ubiquity in both our personal and professional lives, it’s easy to fall into a rhythm with social media, taking a “set it and forget it” attitude and only changing tack when reacting to current events, declining metrics, or requests from colleagues. 

And sure, this can keep your social channels coasting along well enough. However, being more deliberate with your B2B social media channels has the potential to drive stronger results and provide a better customer experience at the same time. One way to do this is to move from a reactive social media strategy to a predictive one.  

What is Reactive Social Media? 

Before we dive in, it’s important to define our terms. In the traditional sense, a reactive approach to social media refers to quickly jumping on trending topics, crafting relevant content that can be posted fast, and cementing your brand firmly within the current zeitgeist. Used as part of your overall social media toolkit, reactivity can:  

  • Help your brand stay relevant 
  • Connect you with new customers  
  • Showcase your creative agility 
By transforming social media data into predictive analytics, it’s possible to forecast how well your next campaign will land. 
By transforming social media data into predictive analytics, it’s possible to forecast how well your next campaign will land. 

When done well, a reactive social strategy can be a boon. For examples of this in action, check out UK smoothie brand innocent on Twitter, who make reactive part of their daily activity, whether it’s commenting on the unpredictability of the British weather or turning their own mistakes into a source of entertaining content. But when reactive marketing doesn’t go to plan, it can be disastrous, as this failed April Fool’s prank from Volkswagen demonstrates. Econsultancy highlights the following risks of reactive social: 

  • Reactive posts can quickly become irrelevant due to time restraints. 
  • There’s less time available for quality control before posts go live. 
  • It’s easier to offend potential customers by capitalizing on bad news, such as natural disasters. 

Despite the risks, when executed correctly, truly reactive social media marketing is a clever way to grab the attention of new audiences and demonstrate your brand’s creativity.  

However, there’s a big difference between this type of opportune, time-bound reactivity and the reactive response of your day-to-day operations. If your current social strategy is often built upon responding to declining metrics, negative feedback, or random requests and suggestions from colleagues (which are usually well-meaning, but not always based on meaningful data), then it may be time to rethink your approach. Predictive analytics can be a useful tool to reboot your social strategy.  

What is Predictive Analytics? 

It’s all well and good to recommend you apply predictive analytics to your social media strategy, but what exactly is it? Predictive analytics is a form of data analysis that uses machine learning and statistical modeling to predict future outcomes by identifying patterns within sets of data. While many industries can and do benefit from predictive analytics, in B2B sales and marketing, it’s an especially powerful way to predict customer behavior and forecast business results. There are several different predictive analytics models. Some of the most useful applications for B2B marketing include: 

  • Forecast Modeling 

Thanks to its versatility, forecast modeling is one of the most common applications of predictive analytics. Forecast modeling estimates numerical values based on historical data. For example, the number of sales a business might make on any given day, or the number of callbacks a sales rep should schedule. 

  • Clustering Modeling 

Clustering models work by categorizing data based on similar attributes within a data set. This is useful for marketers looking to segment their audiences, which can be done based on internal data, such as past brand engagement and purchases, as well as demographic data. 

  • Propensity Modeling 

To evaluate how likely a customer is to take a certain action, propensity modeling is a valuable technique. It can tell you whether a customer is likely to convert, act on an offer, or even disengage. 

Why You Should Use Predictive Analytics in Your Social Media Strategy 

By harnessing some of the techniques mentioned above, B2B marketers can gain a deeper understanding of their target audience and ideal customer profile, plus develop a strategy that aligns with their anticipated behavior. 

And really, you’d be foolish not to. As this article from Marketing Week highlights, with the sheer volume of data now available to marketers about their customers, now is the time to move away from “traditional rear-view techniques” and instead, move toward strategies that push the needle forward. 

Although the rewards of reactive social media marketing can be great, the risks may be even greater. 
Although the rewards of reactive social media marketing can be great, the risks may be even greater. 

Examples of Predictive Analytics in Social Media 

But how do predictive analytics and social media work together? Below are three examples to demonstrate how to incorporate predictive into your social strategy. You may even be putting some of these things into practice already without even knowing it yet. 

Conducting Social Sentiment Analysis 

A successful predictive strategy requires data from which to learn. Looking at standard social media metrics, such as followers, likes, and engagements, is a great starting point—these are useful indicators of campaign, post, and platform performance. However, due to their quantitative nature, they fail to tell you what your audience truly thinks of your brand, products, or services. Luckily, this is where social sentiment analysis can help. 

Social sentiment analysis requires you to conduct social listening across various platforms to discover not just how often your brand gets mentioned, but also what these mentions actually say about you. Armed with this information, it becomes possible to anticipate how well your social campaigns will be received, allowing you to create additional content that promotes positive social sentiment.  

Furthermore, the benefits of sentiment analysis reach beyond social media in terms of their predictive capabilities. For example, if one of your products isn’t performing as well as expected, or customers have noticed a decline in your level of service, social media may be one of the first places they go to express dissatisfaction. This often happens long before they voice these same opinions to your customer service team. But with a successful social listening strategy, you can nip these issues in the bud before they grow any larger. 

Predicting the Future  

Although a crystal ball with the power to predict the outcomes of your sales and marketing efforts doesn’t exist (if only!), taking a predictive approach to social can provide a peek into how certain campaigns and initiatives may perform. After all, it’s been used to forecast the outcome of such huge events as the 2020 US presidential election. 

Using the insights uncovered in your social sentiment analysis, you can predict the outcome of multiple events, such as the popularity of future product launches, the success of your next marketing campaign, or the reaction to a new company merger. By predicting how these events will be received, you can tailor the content you create for social media accordingly. In fact, predicting the types of content that people might like is something you see in action every time you log on to your socials. Think of Twitter’s “Who to Follow” suggestions, or the “People You May Know” feature on Facebook or LinkedIn.  

Being able to anticipate responses in this way also helps with the reactive campaigns we discussed earlier. Having a solid understanding of your audience’s sentiment and regularly analyzing their reactions to your content (versus your predictions) will help you develop a reasonable forecast of how your content will resonate. Therefore, it becomes easier to craft reactive content with confidence, as you can more clearly predict what’s going to land with your audience. 

Creating Lookalike Audiences 

Lookalike audiences are a great example of predictive technology within social media, and likely a tactic you’ve done before while crafting paid social campaigns. Lookalike audiences are precisely what they say they are: audiences with similar attributes to those you’ve already defined. Essentially, they’re cluster modeling in action. These are based on previously successful audience segments, or audiences specifically relevant to your goals. By knowing what’s worked well in the past, it’s possible to predict that this new audience will also yield positive results, thanks to its similarity to high-performing segments.  

The Importance of B2B Social Media 

Keeping abreast of the latest developments within social media is essential, as the majority of B2B professionals discover new vendors via social when conducting research into potential companies to work with. Therefore, it’s important to prioritize a robust social media strategy to attract desirable prospects to your brand.  

If you’d like further help to build your pipeline and get your brand in front of the right people on social media, contact DemandScience today

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