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The current retail landscape is highly volatile. Businesses face various challenges, including operational and supply chain disruptions, shifting consumer patterns, and competition from e-commerce sellers.

Now more than ever, retailers need to leverage data to remain relevant and competitive. Data is critical for strategic planning and building personalized products and services that align with consumer needs, driving customer loyalty, and repeat purchases.

According to the Harvard Business Review, data processing has become increasingly difficult for retailers, many of whom face an information deficit due to the pandemic. Brick-and-mortar retail sales dropped significantly after the outbreak of COVID-19, impacting sales data. Now, retailers are struggling with limited data supplies and compromised data sets.

To survive, retailers need to take stock of their current data inventories, establish a rapidly processing framework, analyze and visualize all of their information.

Datameer has the tools to make this happen.

Datameer Spotlight is a virtual analytics hub that can turn raw data into actionable insights, enabling retailers to consolidate all of their data from disparate sources like POS systems, online stores, customer service touchpoints, marketing programs, and more.

It is how Datameer is helping retailers adapt and thrive in the new normal.

Advanced Customer Behavior Analytics

In-store shopping data is limited due to the pandemic. As such, retailers need to capture customer insights on social media, e-commerce sites, and customer service interactions — like live chat and mobile.

Retailers use Spotlight to aggregate customer behavior data from disparate locations, bringing it together into one self-service platform for rapid analysis and visualization. It enables them to build campaigns and experiences that align with customer expectations.

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Personalized In-Store Experiences

E-commerce has changed the way consumers shop for products. In the not-so-distant past, consumers mostly researched items online and picked them up in stores. Many shoppers choose to explore things in stores and then shop for them online when they get home.
Omni-channel retailers are now using Datameer to process their marketing and merchandising strategies to create highly personalized in-store experiences.

Inventory Management

Inventory management is trickier than ever right now due to widespread product shortages, supply chain issues, and changing consumer spending patterns. Possible shutdown concerns also make it challenging to plan and stock shelves for upcoming seasons.
To streamline inventory management, retailers use Spotlight’s virtual modeling features, making it possible to create advanced and accurate inventory predictions. It enables retailers to make more informed decisions when it comes to choosing which items to stockpile.


Research & Development

R&D teams rely heavily on data when designing and prototyping new products and services. It is a highly collaborative process that requires sharing data and bringing new products to market.
Datameer Spotlight’s sharing and collaboration features enable R&D analysts and engineers to exchange insights and visualize testing results as they move through the development process, making R&D a real community effort.

Customer Conversion

Getting customers to your website is the first step. Getting them to make repeat purchases is the ultimate goal. It requires understanding who is coming to the website, making purchases regularly, and determining why they are spending money with your company.

Use Datameer to create a detailed profile and behavior models – allowing retailers to analyze customer behavior analytics, improving conversion rates and, ultimately, leading to more robust bottom lines.

If you’re looking to become a data-driven retailer to unlock customer trends, increase profitability, drive competitive advantage, Datameer can help. Request a demo of Datameer today to learn more about the easiest ways to unlock the full value of all of your data.

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The future of your organization is hidden in your data.

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