How Predictive Analytics Support Consumer Research

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predictive analytics supports consumer researchToday’s businesses generate enormous amounts of data with relevance to customer interactions as well as insights into consumer behavior. Yet, a recent study reported in the Harvard Business Review finds that more than half of firms aren’t using data to drive business decision-making, a figure that goes up significantly among SME (small and medium-sized enterprises). Such data, especially predictive analytics, support consumer research.

Left to gather figurative dust in cloud storage. data racks up huge bills without providing actionable insights that warrant such expense. For too many, firms are drowning in data and have few insights that they might derive from that data [source].

Further from the HBR article, firms aren’t doing enough with their data, failing to even recognize data as a business asset (53%), let alone devoting sufficient resources to analyzing the data (52%).  Continuing, the authors present evidence that rather than improving, the situation of using data to analyze business problems and provide insights declined over recent years, not increased.

Instead of sitting around collecting fees for storage, big data requires thorough scrutiny, evaluation, and correlation with market estimations. These analyses must then translate the insights gleaned from the data into actionable strategies designed to improve market performance. Predictive analytics support consumer research, as well as improved understanding of actions currently achieving market goals versus those that are not currently optimized for reaching goals.


What is predictive analytics?

Predictive analytics is just what the name implies; using data to predict how consumers might behave in the future, how brands might perform, what locations consumers might prefer, etc. This tool goes well beyond simply describing current or past behavior, to suggest the most appropriate strategies to optimize market performance.

Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future. [source]

predictive analytics support customer research
Image courtesy of Logianalytics

Thus, rather than simple trend analysis, involving weak assumptions that the trajectory of past behavior adequately predicts future behavior, predictive analytics uses more sophisticated techniques designed to ferret out variables that produced the behavior, the extent to which the variable impacted behavior (the weight of the variable’s impact), and careful measurement or estimates of those variables in the future to predict future outcomes (see the image above for a graphical depiction of this process).

With the assistance of this new-age tool, predictive analytics uses a number of statistical packages and modeling techniques, such as machine learning, data mining, and different business intelligence tools including Python, SQL, and others, allowing businesses to arrive at conclusive assessments and projections about prospective openings, opportunities, trends, and market risks. Using these tools also helps businesses maintain a competitive advantage that might last in the long-run.

How predictive analytics support consumer research

By itself, predictive analytics is a powerful tool but it requires trained analysts who have both analytics training and business training to optimize performance. Unfortunately, the gap between the number of analysts required and the number available is huge [source].

While a powerful tool all by itself, predictive analytics supports consumer research better when combined with  Artificial Intelligence, AI, along with machine learning (ML), to generate insights from data. Using AI/ML requires specialized computer skills, business knowledge and an extensive period to train the system, which involves working collaboratively between humans and machines.

Showing immense promise, AI/ML empowers predictive analysis in market research, helping it make a stand-out reputation for itself. Combining these factors boosts the power of predictive analysis, especially in the domain of market research. The combination of AI and analytics fueled the growth of firms like Amazon, which now dominates the online market.

Despite the potential benefit of combining AI with predictive analytics, utilization lags, not only because there are too few analysts able to harness the power, but because:

  • Much of the data is unstructured, meaning it involves words and images that machines are hard-pressed to categorize. Human language is so nuanced that, taken out of context and with no non-verbal indicators, even humans have trouble accurately interpreting meaning.
  • Confusing correlation with causation. Two variables might move together, correlation, but that doesn’t mean that one variable caused the change in the other variable. We call it a spurious correlation when the variables both change when the change in one has nothing to do with a change in the other.
  • Unsophisticated nature of the involved procedures

Furthermore, businesses have taken an interest in the application of Business Intelligence tools in tandem with using cloud technologies. Together, both spheres are gaining in prominence in the domain of big data analytics.  Geared with new technologies, the market research domain is making its way towards being more efficient, accurate with data collection opportunities at low price points.

Application of predictive analysis in market research

In the foreseeable future, the application of predictive analysis driven by AI in the market research sector will increase because its potential is too great to ignore. As more and more businesses welcome the concept of automating their processes with the assistance of machine learning algorithms and intelligent models, barriers to entry go down.

So, in an organizational landscape, what does that generally include?

Survey Designing:

A great way for businesses to understand areas for improvement involves conducting regular surveysYou must know your customers and prospects; their likes, their media habits, their lifestyles, to best market your products. Therefore, implementing traditionally structured surveys through the usage of market research templates has a very important effect on market performance. Crafting and analyzing surveys, both an art and science, is costly, time-consuming, and fraught with opportunities for abuse. Take, for example, the mistake Coke made in interpreting survey results leading to the introduction and later elimination of New Coke.

In fact, traditional surveys, with pre-coded response options, restrict the research. The outcome, hence, is sometimes skewed as an evaluation of this predefined hypothesis, which doesn’t help develop insights of any value. In the Coke situation, researchers assumed sweetness was a key aspect leading consumers to choose a cola brand. They used this information to increase the sweetness of their New Coke product. However, must to the company’s embarrassment, nostalgia was more impactful on purchase decisions than sweetness and the change was viewed as decreasing the appeal of the brand.

To achieve more successful outcomes in your surveys involves open-ended questions, which permit a deeper interpretation of results. However, analysis of this unstructured data is challenging. Digital tools help by performing more accurate assessments. AI, with its smart algorithms, analyses this data much quicker, and, with adequate training, can trigger fast and precise analysis of all unstructured data to accelerate time-to-insights.

In the AI/ML age, that might mean using tools to craft templates used over and over again with modifications based on prior survey results rather than the time-consuming process of human survey design. If not viable, a number of online survey software exist that can also get the job done.


Automated text analysis:

Text analysis powered by AI is the real game-changer here. It has the potential to reshape the market research sector by the application of automated text analysis. Other features include custom-designed question development and interpretation of outcomes.

With access to deep learning-driven text analysis algorithms, AI, combined with NLP (natural language processing), enables analysts to detect fundamental issues of interest within a text string, while analyzing in context. With predictive analysis, analysts also filter and sort through messages that are related contextually to key research themes. This combination also recognizes the underlying sentiment of the users related to the themes.

Enhanced data accuracy:

Predictive analysis is essential in mining precise data that minimizes the opportunity of human bias.

There is an increased possibility of bias appearing on the report if a human interprets the data because humans are designed to look for positive confirmation of their beliefs. Hence, testing a hypothesis using the ambiguity inevitable in unstructured data, humans are more likely to find confirmation than disconfirmation. On the other hand, unstructured data processed through AI eliminate all forms of human bias from the report, thereby, automatically enhancing data accuracy.

Automated report generation:

With the increasing possibilities of AI’s ability towards comprehending various qualitative and quantitative data, its programs are designed to now produce personalized reports based on a given set of keywords or subject areas.

Quick insights:

Businesses demand a quick turnaround of insights generated through accurate data mining from accumulated research. With an instantaneous influx of insights, Predictive analytics complements agile decision-making as it is no longer a singular activity. Instead, it is linked with a series of micro-decisions powered by AI and machine learning that helps in aggregating information from a number of systems.

Filtering through topics from individual updates and structuring information in an organized fashion makes the process of digesting and stimulating decision-making and human thinking that much more responsive.

Conclusion

Recently, predictive analytics gained prominence with increasing data, computer power, and tools like AI/ML. But the next few years will see predictive analytics rise given its potential to dramatically redesign the nature of the overall business landscape and market research segment. By simplifying the user experience in terms of access to insights exponentially increases growth, expansion, creation of opportunities within market research.

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