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:
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 The data is historical; it is stagnant and quickly outdated.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.
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.
The award-winning Signals Analytics 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.
Today the company announced a new rollout of its award-winning platform, leveraging breakthroughs in NLP, machine learning and other capabilities that enhance the precision, detail and utilization of advanced analytics in the enterprise. 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 Signals Analytics 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.
“With today’s announcement, we are answering a call from the market for more open platforms that increase the impact of analytics across the enterprise, with greater flexibility to support multiple use cases while reducing the reliance on in-house analytic, data science and machine learning expertise. 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,” said Gil Sadeh, Signals Analytics Co-Founder and Chief Executive Officer.
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