Data-Driven
Retail Insights
360° Customer Insights
Integrate all of your customer data to boost analytics and deliver personalized, multichannel customer support.
Optimized Business Flow
Enable consistent, real-time flow of information to ensure that every part of your business is on the same page.
Omnichannel Brand Experience
Optimize and synchronize on your in-store, mobile and web experiences for increased brand unity and improved customer experience.
Common Problems in
Retail Industry
Siloed Data
For many retail companies, data is stored across various legacy systems that were never designed to communicate with one another. This leads to siloed data, with things like customer loyalty data, transaction data, social media data, online purchase data, CRM information, and loyalty card data all living apart from one another.
More Effective Customer Support
Get more accurate segmentation and targeting - and use this to improve your customer service, avoid churn, and respond to customer queries immediately and accurately.
Better Inventory management
Check product availability by channel and use real-time data to move inventory where it’s needed ahead of time. Additionally, use predictive analytics to decide what to stock and where based on data about regional differences in preferences, weather, etc.
Smart revenue forecasting
Get more accurate forecasts based on the predicted buying habits of brand new customers instead of forecasting revenue based on historical data from shoppers who may not even be customers anymore.
Better Marketing and Sales Campaigns
Use comprehensive insights and increased segmentation to improve your marketing and sales campaigns.
High Volume of Data
Retailers have more data coming in than maybe any other industry, and the sheer volume, velocity and variability of the data poses a big problem when it comes to analytics and insight. More often than not, just getting the content into a relational database is nearly impossible: to fit all of the data into one schema is costly, time-consuming and extremely difficult, and many retailers fail to complete even this first step.
Improve Vendor and Supply Chain Management
Combine structured and unstructured data in real time to make more accurate forecasts or enable automations like automatic reordering.
More efficient inventory management
Manage your inventory based on real-time data and behavior. Centralize the data from your storefronts, website, and backend to ensure that your numbers inventory data is consistent and error-free.
Optimized pricing strategies
For retailers to optimize their pricing strategies, they must first know the 5 W’s; the where, when, what, who, and why of store-level purchases. Integrating all relevant customer data ensures that all 5 W’s are answered and that effective strategies are put in place.
Analysis Based on Historical Data
With legacy systems and more outdated models, retailers look back at shoppers’ past activity to predict what they will do next. This isn’t a good indication of future activity, and it could lead to poorly targeted strategies.
Prediction and Machine Learning in Real Time
Use real-time prediction based on current trends and behaviors from all sources of data. While this kind of predictive analytics may seem easier for eCommerce stores, even brick-and-mortar retailers can use real-time data to inform their decisions and better target specific customers.
Anticipate Customer Activity
Make specific recommendations for certain customers on what they might like based on past purchases or even their social networks (i.e., recommending products their friends also bought).
Create a More Agile Business Based on Up-to-the-Minute Signals
Machine learning and data-driven predictions - looking at both your own company and your industry - can help you avoid mistakes you’ve made in the past and create more effective long-term strategies.
Adapt Automatically With Customer Behavior
By automating your marketing and sales tools and effectively interacting with your customers, you increase long-term retention, reduce churn, and improve customer satisfaction.
Siloed Data
For many retail companies, data is stored across various legacy systems that were never designed to communicate with one another. This leads to siloed data, with things like customer loyalty data, transaction data, social media data, online purchase data, CRM information, and loyalty card data all living apart from one another.
Read how Integrate.io can helpHigh Volume of Data
Retailers have more data coming in than maybe any other industry, and the sheer volume, velocity and variability of the data poses a big problem when it comes to analytics and insight. More often than not, just getting the content into a relational database is nearly impossible: to fit all of the data into one schema is costly, time-consuming and extremely difficult, and many retailers fail to complete even this first step.
Read how Integrate.io can helpAnalysis Based on Historical Data
With legacy systems and more outdated models, retailers look back at shoppers’ past activity to predict what they will do next. This isn’t a good indication of future activity, and it could lead to poorly targeted strategies.
Read how Integrate.io can help"Integrate.io makes it much easier to extract and analyze data from disparate systems. More importantly, it gives us back our time to focus on making decisions and taking action based on insights."
Senior Business Analyst
at iCharts
Customer Spotlight
Raise.me can transform their data without engineering resources, allowing their entire team to be more productive and efficient.
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