Any salesperson who’s been around the block for some time will tell that most prospects will never express their intention to buy a product. Sales reps, therefore, look for certain behavioral traits or buying signals when interacting with the customer. Buying signals can be defined as any sign or cues from the customer indicating his/her intent to purchase. The signals may be verbal or non-verbal and can be seen across various stages of the customer’s purchase journey. The more obvious buying signals, such as enquiring about prices and discounts, or asking the sales person to explain the offerings or a product’s specification, can give the sales reps an indication regarding the buyer’s interest and accordingly take the right approach to closing the deal. However, when these cues are subtle, the sales team, without even realising it, can lose out on major opportunities.
In the current scenario, as consumers are increasingly gravitating towards digital buying channels and online transactions, the traditional way of anticipating buying signals doesn’t work any longer. Assessing and analysing consumers’ digital buying behaviour demands slightly more advanced solutions like Predictive Intelligence, that can aid marketers to identify positive buying behaviour. Modern technologies like Artificial Intelligence and Machine Learning are driving this paradigm shift in marketing, giving brands the power to detect buying signs among customers by analysing and interpreting large volumes of data. Let us, then, take a look at how exactly AI does this:
Through The AI’s Looking Glass: Identifying Buying Signals In Customers With Predictive Intelligence
Digitisation has enabled instant feedback — a great advantage to brands and marketers who need to constantly communicate with consumers today to keep them engaged. For instance, consider someone is looking to buy a new laptop and searches for PCs on Google. PC brands and sellers will be notified of this ‘trigger event’ and capitalise on this opportunity by providing suggestions for computers that meet the requirements of the customer. Let’s say the consumer frequently books air tickets and travels often. AI and machine-based predictive intelligence models will analyse and interpret this information to offer product recommendations for laptops that are suitable for travel.
As the above example illustrates, predictive intelligence models function by simply breaking down all such data into two categories, namely internal data and external data. A company’s internal customer data includes purchase histories, firmographics, seasonal buying patterns, purchase sequence patterns, sales logs, and customers’ website activities. Thus, the relevant key analytical tests for deriving insights from internal data include:
From sales calls, online query forms, emails, etc., sophisticated machine learning and predictive analytics algorithms have the ability to compile and scrutinise volumes of data to analyse a prospect’s questions, word choice, tone, sentences, etc. Such in-depth information analysis can aid sales reps to leverage a potential client’s pressure points and approach them based on how they react to communication from the brand.
Purchase History, Cyclicity, And Sequence Analysis
Past purchase behaviour can serve as a useful indicator for future activity. For instance, transactions during and around festivals indicates a surge in purchases during specific time periods. Moreover, the dates on which a particular product has been purchased can help brands plan their sales and marketing campaigns for the next purchase cycle, while at the same time, pitch other allied products to the customer.
While the above analytical tests are driven by internal data, there are others that feed on external data to derive insights on consumer purchase behaviour. External data is anything that comes from outside a company’s records. This usually includes publicly available information, such as news about the buyer, company information, and buyers’ social media activities. Additionally, information from third-party sources can enrich internal data and help sales teams get a complete view of the prospect. Some of these are:
Many businesses publicly release information about the recent purchases done, business acquisitions, partnerships, and newly-acquired big-name clients. In the short term, such information serves as an indicator for potential purchases, allowing brands to strategise and target these consumers.
Social Media Activity Analytics
Social media has emerged as the most dynamic playing ground to test predictive intelligence tools and see how they impact their marketing goals and outcomes. Interaction with customers on social media, such as comments, feedbacks, likes, and shares, helps marketers focus their microtargeted sales efforts on a specific set of consumers. Furthermore, sentiment and text analysis models can be used on prospects’ social media posts to see how a potential customer reacts to targeted communications. This insight can then be used to guide the seller with how to approach their target audience and provide them what they want.
While AI, machine learning and other predictive intelligence tools are not the last word on how brands can augment sales, the contribution of these tools is considerable enough to make their sales efforts more effective. An AI driven sales strategy can, therefore, help streamline the marketing process of brands, allowing for more precise targeting and segmentation. As a result, sales reps will have the ability to close a greater number of deals in much lesser time.
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Titir Pal is Head of Products & Solutions at Absolutdata Analytics