Every retailer is facing a similar challenge. If you are a retailer and constantly feel the pinch from online giants like Amazon and Google, you have an opportunity to gain back control and competitive advantage with more personalized products and services, building that intimate relationship that these giants simply cannot provide.

Source: https://searchenginewatch.com/2016/09/27/more-online-product-searches-start-on-amazon-than-google/

Just as Steve Jobs reinvented an entire industry in 2007 when he introduced a mobile gadget called the iPhone, retailers have an opportunity to reinvent themselves by leveraging data generated by the mobile shopper. The mobile phone is the center of the retail universe and every click generates valuable data that can be used to make predictions to stay in front of consumers wherever they may be. For example, when tracking the mobile shopper or omnichannel buying journey such as customer online activity, stores they are visiting, and what they are tweeting about, organizations can adjust promotions for higher profitability.

In this blog, we discuss the need for a modern BI and analytics platform that has agility to move at the same pace with the omnichannel consumer so decision makers are able to make the right adjustments to products, inventory, pricing, promotions and more. For instance, location-based promotional activities are hugely popular amongst retailers seeking to drive traffic to their stores, using an online coupon that can be redeemed in store. Apart from the obvious benefit of increasing foot traffic, the in-store coupon gives retailers a perfect opportunity to introduce new products and services to customer identified as more eager to take advantage of a discount. This customer segment identified as most likely to purchase new products and services is uncovered through the analysis of diverse data sources such as customer service, social media, market data, and more. This is just one example of how data and analytics enable businesses to anticipate a mobile shopper’s every move and keep them happy and loyal.

Watch the on-demand webinar to learn how Birst Networked BI takes an agile approach to connecting data, analytics, and users across the organization to better analyze and adjust to your customer.

I spoke to Infor’s Director of Retail Strategy Matt Simonsen to discuss how customers should think about using data and analytics to uncover new strategies to grow in a highly competitive retail market.

1. What is your role at Infor and goals for the Retail business?

I am responsible for the global retail business for solutions focused around finances, people, assets, and data. My goal is to help establish Infor Retail as a trusted advisor assisting retailers and fashion brands on their digital journey.

2. What business units in Retail are ripe for gaining insights from the mobile shopper and why? What is preventing them in doing this?

All retail business units are tied to the modern, mobile shopper and in need of better analytics to better understand shopping behavior. Providing an omnichannel experience and expanded fulfillment options for the customer are fundamentally driving change across retailers. The solutions needed to serve the mobile shopper require more coordination between business units than ever before.

For example, Marketing, Merchandising and Supply Chain must be all coordinated when running promotions to meet the overall organization’s performance goals around revenue and profit. If Marketing and Merchandising are measured on revenue, but do not have accurate information of the replenishment cycle, a promotion may be run where the revenue goal is met at the expense of Supply Chain increasing costs for expedited shipping and overtime charges.

Unfortunately, this coordination is challenging, as data remains siloed inside most retail organizations. Yes, they have enterprise data warehouses and reporting tools, but they are slow to implement and not agile enough to adjust with changing business dynamics. Omnichannel introduces more customer data in different formats that make it challenging for the data warehouse to capture and deliver to end users. As a result, retailers setup reporting units inside of each business unit to handle their need for quicker insights or better understand what key factors leads customers to purchase. Data never reconciles at an enterprise level, leaving leaders making decisions based on incomplete and inaccurate performance metrics.

3. Retailers rely on daily, operational reporting first thing in the morning to assist in making decisions for the day. Where are retailers at in advancing from operational reporting to predictive analytics to further improve decision making? What is holding them back?

Retail is a nimble game and will always require operational reporting and daily adjustments. Predictive reporting is not widely adopted today and wouldn’t be very accurate if attempted with today’s disconnected reporting environments.

For retailers to utilize predictive analytics they will need to migrate from legacy, on-premises solutions to a modern BI and analytic platform that is cloud-native and connected. Leveraging the elastic computing power of the cloud will allow for both enterprise data (sales, store traffic, etc.) and external data (weather, traffic, local events, etc.) to better inform predictions of future needs, accessible by all departments. We do see retailers investing in machine learning solutions to improve forecasts, allocations, and replenishments by leveraging cloud-native solutions to crunch massive amounts of data.

4. Retailers are filing for bankruptcy at record-high rates as Americans’ changing shopping habits, along with years of overly aggressive store growth, continue to shake up the industry. What is your take on how this could have been avoided with data and analytics and your advice for current retailers?

Changes in technology leads to changes in business models. Changes in business models leads to changes in the operations, people, and strategies. This requires an agile BI and analytics tool to assess, monitor, and adjust.

The main reason for the recent wave of bankruptcies is the struggle to address omnichannel challenges. There is a lot of strain put on each business unit to keep track of the activity in different channels and coordinate with other businesses to understand the cross-effects of activity across the organization and across channels. It’s a huge challenge and one that cannot be successfully done with poor analytics. Most retailers are struggling with the analytics, but the retailers without the scale to weather the storm are normally the ones who suffer first.Better analytics could have helped re-direct attention and investment to the most profitable channels and strategies available to a retailer. The world changed and the retailers who struggled to adapt their business models because of a lack of analytics or lack of will suffered.

Best Buy is an example of a retailer successfully changing strategies due to new market dynamics. They embraced the fact that their stores were showroom floors and found new strategies around showrooming to acquire and retain customers. Price matching, post-sale installation services, higher quality staff, and more were all new initiatives that required an agile BI and analytics tool to monitor, measure, and adjust. For example, if price matching is affecting revenue goals, Best Buy may need to look for new in-store revenue streams.

5. There is no dispute that data, analytics, and now machine learning is key for retailers to reinvent themselves and gain competitive advantage. With the acquisition of Birst last year, a leading Cloud BI and Analytics platform, how are retail customers reinventing themselves?

Retailers who are leveraging the power of the cloud and connecting their business with other participants in the value chain will be the winners in the future. Birst Networked BI creates a connected enterprise to help Retailers future-proof their operation and assimilate new technologies like AI and ML into their operations.

It can be a bit confusing and overwhelming to know where to start, what data to collect and how to analyze to achieve real business outcomes. Birst offers two very powerful and unique ways to get results from data generated by mobile shoppers: (1) Networked BI solution that connects Marketing, Sales, Inventory and the entire organization to quickly analyze omnichannel behavior to drive and adjust retail strategies, (2) access to expertise in grocery, chain drugstores, fashion, luxury goods, and restaurants to create an analytics roadmap that takes advantage of the right use cases that deliver results.

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Mona Patel works in Birst’s Product Strategy team. With more than 20 years of experience building analytic solutions at The Department of Water and Power, Air Touch Communications, Oracle, MicroStrategy, EMC and IBM, Mona is now growing her career at Birst. Mona received her Bachelor of Science degree in Electrical Engineering from UCLA.