Traditional market intelligence uses a set of metrics to measure past performance and guide business planning. It consists of querying and reporting to answer “what” questions, namely the first 4 W’s:
- Who did it?
- What happened?
- When did they do it?
- Where did they do it (on what channel)?
There are some very obvious limitations. The data is historical; it is stagnant and quickly outdated. It tends to be based on internal reporting information or focus group research that is expensive, time and resource intensive and typically has many inherent forms of biases that are hard to parse out of results. In addition, traditional market intelligence also tends to be limited to a particular data set or question to analyze.
Social listening platforms emerged as a way to monitor social media mentions and conversations for customer feedback, direct mentions and discussions related to specific keywords, topics, competitors or industries, and the idea is to use that information to conduct analysis and find better ways to meet customer needs. However, much like traditional market intelligence, social listening platforms also tend to be quite quantitative, and limited only to the consumer base that is engaged with Twitter, Facebook, Instagram and the like, but it’s the qualitative insights that really make a difference.
The 5th W: The Why
Qualitative research gets to the why.
It unearths opinions, thoughts and feelings of consumers and leads to the “aha” moments in the decision-making process that informs new concepts, products and marketing strategies. Understanding the “why” gets you more aligned with the consumer to better meet their needs, enhance their experience and foster brand loyalty. Gaining that understanding from online sources has been a challenge for business executives. The online world is vast, never-ending, unstructured, constantly changing, sarcastic, full of hyperbole, emotion and emojis, and disconnected. It takes unique capabilities to break through the clutter. This is the future.
Skai Powers the Future of Market Intelligence
The award-winning Skai platform encompasses main components of a data fabric: the data collection layer, the data classification layer, and the data access layer that are all configurable to meet the business needs. More than 13,000 data sources get connected and harmonized by the analytic engine, which extracts context and sentiment and generates insights that are presented in more than 100 business-ready analytic models and apps.
Because the insights generated by the platform are extremely accurate and contextualized, they help to shorten product development lifecycles, reduce the timelines associated with market intelligence and competitive positioning, and maximize the effectiveness of business decisions and marketing strategies, all of which brings brands closer to their customers, giving them first mover advantage in predicting and reacting to trends.
New features that span the entire data journey such as e-commerce product clustering, author, affiliation, and brand refinement, data mart integration and a daily alert allow businesses to seamlessly integrate Skai into their existing business intelligence technology stacks, while surfacing highly-detailed and predictive actionable intelligence across a broader range of use cases, increasing the impact of analytics throughout the enterprise.
New NLP and automatic machine learning (auto ML) engines improve data collection and classification and help address unique external data analytic challenges, such as product clustering, author, affiliation, and brand refinement.
Product clustering is a capability that identifies products that are the same but have different names across different e-tailers, preventing skewed analytic results. With greater than 90% recognition accuracy, organizations can configure this capability and tailor predictive analytics outcomes to align with their business. This means more prescient decision-making that lowers risk and maximizes business outcomes.
Author, affiliation and brand refinement capabilities are part of a set of new named entity recognition (NER) features that allow accurate representation models for precisely identifying authors, research institutions and FMCG brands within a large depository. The model compares the authors and brands of each new research paper, patent, or clinical trial, and determines if there is a match or if the representation model should be augmented.
Expanded openness of the platform through the new Data Mart integration offers a direct connection to Signals Analytics’ connected and classified data sets for organizations to conduct analytics in their own business intelligence environments.
Extending the connectivity and openness of the platform helps our customers move away from cumbersome analytics projects, while still fulfilling their need to be data-driven and precise in their decision-making process.
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*This blog post originally appeared on Signals-Analytics.com. Kenshoo acquired Signals-Analytics in December 2020. Read the press release.
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