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Spy Stream 2.0 Review: Ground Breaking Behaviour Spy Technology

The consequences of good intentions

If you're trying to understand customers' behaviors, perhaps the last people you should ask are your customers.

A traditional marketing approach for predicting consumer behavior is to elicit behavioral intention instead of actual behavior. Frederick F. Reichheld, developer of the very popular Net Promoter Score (NPS), based his loyalty system on a customer survey that asks how likely they are to recommend a company's product or service to their friends.

While this Spy Stream 2.0 Review is nice to know, if I'm responsible for my company's marketing strategy, I'm more interested in whether the customer recommended our products to their friends, if their friends bought something from our company as a result, and if their friends had a good experience dealing with our company. In order to get this information, you'll have better luck dealing with your data scientists than your customers. When it comes to understanding and, more importantly, predicting consumer behavior, your big data analytics team will have better answers than your customers.

The feasibility of collecting data on actual consumer behavior

A big data analytics approach that studies actual behavior should supersede the outmoded approach of focusing on behavioral intention. The underpinnings for the behavioral intention approach come from an expectancy-value model known as the Theory of Reasoned Action(TORA). In this model, behavior is a function of behavioral intent, which is in turn affected by attitudes and subjective norms. The idea is to affect behavior by adjusting proposed levers that affect attitudes and/or subjective norms. When skilled marketers tell you that, "Friends don't let friends drive drunk," they're attempting to adjust your normative beliefs through your peers. The classic measurement system for this model is behavioral intention — not actual behavior — chiefly because it has traditionally been the easiest information to collect. Collecting data on actual consumer behavior has typically been unfeasible and impractical — until now.

The emergence of the Internet, e-commerce, and social media has radically altered the landscape of available consumer behavior data. Cash registers and Point-of-Sale (POS) systems are being replaced by e-commerce sites that record every move consumers make — even when they don't buy something. Casual telephone conversations with girlfriends about recent purchases are being replaced by tweets that can be scanned and analyzed by anyone who follows those Twitter feeds. All of this wonderful data on actual consumer behavior and experiences is there to be measured and analyzed, but there's a catch: You must embrace the new way of thinking that customers can tell you what they think, while data scientists can tell you what those customers actually do.

Big data and social media analytics

The strategy to employ with your big data strategy team is to measure and analyze actual behavior. There's nothing wrong with Viking Email Marketing PLR Review or measuring behavioral intention for that matter, though the key performance indicator (KPI) centers on actual behavior.

If you sell products online, your big data analytics team should look for digital behaviors that are conducive to your strategic objectives. I specialize in loyalty marketing, so when I'm trying to help a client build a loyal customer base, I look for digital behaviors that indicate a statistically significant level of engagement. Ideally, this is something I can measure directly from the operational data (e.g., web logs). I don't need to ask customers if they love my client's products, because I can see it in their digital behavior.

Big data analytics can also help in understanding the conversation that's happening around your customers' experiences with social media analytics. This is similar to surveying for behavioral intention, but it's more authentic. Although focus groups are designed to elicit open and honest feedback from your customers, it's much better to eavesdrop on a social conversation about your products in real-time.

Coalescing all of this consumer data helps build the bigger consumer experience picture that you should strive to attain. However, none of this is possible without engaging a sharp big data analytics team that can crunch through the complexities of the raw data and translate it into consumer insights.

Conclusion

Today's marketing strategy requires 21st century thinking, which unequivocally involves big data analytics. Behavioral intention is an outdated proxy that's superfluous now that actual behavior can be measured. Measuring actual behavior instead of behavioral intent will dramatically increase your marketing effectiveness.

You should take some time today to see if your big data analytics team can model desired consumer behavior from your operational data. And, abandon the road paved with good consumer intentions — I think you know where that leads.

TDWI research consistently reveals that a major driver for analytics, and especially advanced analytics such as predictive analytics, is to deepen an understanding of customer behavior. In fact, marketing and sales are often the first departments to start using advanced analytics. These groups want to do more than understand and gain insight into customer behavior. They want to engage, retain, and strengthen bonds with these customers.

Gain insight: Clearly, one of the main reasons organizations analyze data is to gain insight. Exploring your data for insights about customer behavior may involve segmenting your customer base, which often uses cluster analysis, a technique that organizes a set of observations into two or more groups that are mutually exclusive based on combinations of variables.

Typically, organizations do discovery and segmentation analysis using structured data. However, unstructured text data, such as social media data or internal text data, can also provide great insight into customer sentiment and behavior. More often, organizations are performing social media analytics, such as voice-of-the-customer analysis, using text analytics technology to gain insight about what customers are saying and how their brand resonates with existing and potential customers. TDWI sees increasing interest in these technologies.

Attract and engage: If you’ve segmented your customer base, you can target customers and engage them because you have a better sense of what they might be interested in. For instance, an organization wants to make customers the right offer when it launches a product campaign across various channels (online, e-mail, mobile, in-store, etc.). By analyzing historical purchases and profiles, companies can predict the likelihood, or propensity, of future activity at a customer level. For instance, a company might use a propensity model and past purchase behavior to gauge the probability of a customer making a certain purchase. This data can be used when developing the new campaign.

Improve retention: Customer retention is a key marketing activity, especially when it comes to profitable customers, and predictive analytics can be extremely helpful. For instance, decision trees can be useful where there are discrete target or outcome variables of interest (leave or stay, for example). Typically, a set of historical training data is provided to the predictive analytics algorithm. The data might consist of different kinds of information about customers (demographics, purchase history, even past sentiment) and it is used by the decision-tree algorithm to determine decision rules that describe the relationship between the input and outcome variables. These rules can be used against new data where the outcome is not known (for instance, leave or stay). These models are often operationalized -- for instance, in a call-center where agents can use them to try to retain customers at risk.

Strengthen bonds with customers: Organizations want to continually strengthen relationships with their existing customers while attracting new ones. Customer lifetime value models help organizations understand the future worth of customers and segments. It is an important part of a customer strategy. Techniques like affinity analysis using market basket analysis to understand combinations of products bought together can be very useful in driving e-mail marketing and recommendation engines.

Of course, I’m only scratching the surface here, and many of these techniques can be used in multiple situations. If you want to learn more about how successful organizations are using customer analytics, think about attending the TDWI Executive Summit in San Diego September 21–23! We’ll be talking specifically about customer analytics and the data and infrastructure to support these analytics.

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