As the industry is turning towards a more digitized approach, retailers are implementing advanced analytics practices to address major business challenges and to maximize benefits. Predictive models enable businesses to determine the intent of their customers, which assists retailers in meeting market demand. Predictive analytics monitors historical product pricing, the interest of the customer, competitor’s pricing, and inventory to deliver best possible prices to gain maximum profits to organizations. For instance, in Amazon’s marketplace, sellers that are using algorithmic pricing are benefitted through better visibility, sales, and timely customer feedbacks. Furthermore, according to the retail intelligence company Upstream Commerce, an automated predictive and dynamic pricing tool delivers up to an additional 20% net profit gain.
Predictive analytics is a key solution that helps retails and eCommerce businesses in building smarter market strategy and also helps in faster decision making. It helps in making significant enhancements in the business operations which include improved customer engagements, identifying better targets prospects, price optimization, and predictive inventory management. Further, with the added ability to deliver more effective marketing higher profits and more controlled operations, predictive analytics have become a critical component to both eCommerce and retail businesses.
COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY
The vendors for predictive analytics software in Retail and Ecommerce are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 26 vendors evaluated in the data quality tools market include Opera Solutions, Domino Data Lab, Dataiku, Figure Eight Inc, Civis Analytics, Agilone, Alteryx, Inc, Fair Isaac Corporation (Fico), Information Builders, Inc., Qlik Technologies Inc, Microsoft Corporation, Teradata Corporation, Knime Ag, Kognitio, Microstrategy Incorporated, Exago, Inc., NTT Data Corporation, Rapidminer, Inc, Greenwave Systems, Inc, Tibco Software Inc, Sisense, 6sense, Lytics, Good Data, Radius Intelligence Inc. and Angoss Software Corporation.
Use Cases of Predictive Analytics Software in Retail
Social Media Analysis: With increasing digitalization, retailers are moving towards promoting their products through social media platform. As a result of that, retailers need to monitor online sentiment and respond in real time with relevant messages or offers. In addition, consumers are also using social media to exert tremendous influence over a retailer’s brand or a product’s success.
BEHAVIORAL ANALYTICS: Retailers are focusing towards improving customer conversion rates and personalizing marketing campaigns avoiding customer churn, and lowering customer acquisition costs to increase their revenue.
PERSONALIZE IN-STORE EXPERIENCE: With increasing ecommerce/ online sales, retailers are focusing towards providing personalized in store experience to establish and drive loyalty by giving offers to incentivize frequent consumers to make more purchases thereby achieving higher sales across all channels.
CUSTOMER JOURNEY ANALYTICS: with increasing competition and complex retail queries such as understanding activities on every step in the customer journey, understanding customer behavior and best possible to reach them, companies are adoption predictive analytics solutions.
OPERATION AND SUPPLY CHAINS ANALYTICS: Faster product life cycles and ever-complex operations tend drive the focus to retailers to use predictive analytics solutions to understand supply chains and product distribution to reduce costs and gain competitive advantage.
TRADE PROMOTIONS OPTIMIZATION: Many companies are losing over one-thirds of the money invested in trade promotions to provide awareness among customers. This is mainly due to inability of decision-makers to measure trade promotion effectiveness and return on investment to profitably optimize spend by leveraging data.
Case Studies of Predictive Analytics Software in Retail
Versium AnalyticsCase Study: Verium helped Outerwall to determine location for new kiosks using large volumes of consumer and sales data
Versium Analytics helped US based retailer, Outerwall in predicting the best locations for 20,000 new retail kiosk using its platform, LifeData. The platform helps in delivering a significant volume of highly relevant and non-biased insights into Outerwall’s customers in 10% of the time than the other methodologies such as cluster survey.
- 90% decrease in data analysis time
- Delivered historical activity data of over 250,000 transactions representing approximately 100,000 customers from their existing kiosks
BRIGDGEi2i AnalyticsCase Study: BRIDGEi2i helped India’s largest retailers to study customer’s buying pattern, create targeted promotions, and enhance sales value
India’s largest retailers are implementing numerous methods to understand customers’ intent, provide instant response to changing customer expectations, determine future customer behavior, and bridge the digital and physical shopping experiences, thereby boosting the overall sales. Retailers have increased their focus on digital transformation tools, such as customer intelligence and predictive analytics, to deliver an enhanced, personalized customer experience and meet in-store expectations.
BRIDGEi2i’s predictive analytics solutions helped these retailers derive actionable insights on changing customer behaviors and develop customer-centric approaches to maximize customer retention. The company offered the ExTrack proprietary platform to effectively track customer experience-related issues and correlate these issues to enhance business outcome.
- ExTrack accelerated the customer experience in a quick timeframe
- Provided 360° customer view
- Discovered areas that needed instant attention
- Built customer loyalty and personalized schemes
SAP SECase Study: SAP SE helped Grupo Merza to gain insights into market baskets across products, categories, and stores
Grupo Merza, a retail and wholesale distributor headquartered in Michocan, Mexico, offers numerous products and services, such as food and beverage distribution, transportation and logistics, and financial services. The group employees a workforce of more than 4500 people who operate in 19 wholesale distribution centers and 152 retail chains across Mexico. The group desired to augment its analytical insights and enhance efficiency for inventory management, transportation, delivery, and crediting and invoicing.
The company deployed the SAP HANA platform, SAP Lumira software, SAP Sales Insights for Retail analytics application, and SAP Predictive Analysis software to gain a competitive edge by easily understanding customer needs, increasing sales, and improving customer engagement. The company took only 4 weeks to deploy the SAP Lumira software without involving any consulting services.
- Improved the transactional data and reporting delivery
- Ensured faster decisions with self-service data visualization
- Provided insights into how product assortment and promotion contribute to market baskets
- Facilitated in recognizing the defaulters who had not cleared their debts
- Created scorecards which would be instrumental in predicting future lenders’ behaviors
Arjuna SolutionsCase Study: Arjuna Solutions helped First Book to analyze personalities and behavioral patterns of its customers
First Book, an international distributor of high-quality books to disadvantaged youth. The company offers platform teachers and school administrators to register for an account and purchase books at substantially reduced prices on behalf of students. With increasing number of account holders, First Book struggled to increase repeat sales and customer engagement.
First Book, used Persanalytix to identify the common characteristics among its customers, predict their individual customers who are likely to engage in repeat sales.
- 720% Increase in Purchases from Email Marketing
- 331% Increase in Repeat Sales Success Rate
- 11 Billion Data Points Cleansed, Integrated and Supplemented
- 97% Accuracy Predicting Individual Customer Spend
SASCase Study: SAS helped 1-800-FLOWERS.COM to Analyze data in real time to help improve the customer experience
1-800-FLOWERS.COM, the US based floral and gourmet foods gift retailer and distribution Company is focusing on enhancing customer relationship, increases customer lifetime spending and encourages cross-brand shopping. To analyze data in real time to help improve the customer experience, the company is using SAS Business Analytics solution.
- Reduced customer complaints by 40% during the critical Mother's Day season and increased customer satisfaction
- Increased in new customer order
- Initiated the Perfect Order Every Time (POET) program
Tibco SoftwareCase Study: TIBCO helped Yakult to enhance its new product sales in the Netherland
Yakult, Japan based leading probiotic beverage Company. The company’s portfolio includes a range of consumer, cosmetic, and pharmaceutical products. The company was suffering from time-consuming analysis, mistakes, and spreadsheets. The company used TIBCO Spotfire which helps in distinguish sales drivers from non-drivers in a very dynamic environment. Yakult was able to identify the elements in its marketing mix that drove the sudden category growth. Applying this knowledge to future marketing budget decisions fueled additional growth
- Sales Increased by 15 % to 20%
Teradata SoftwareCase Study: Teradata helped largest beverage producers to monetize, manage and Increase sales globally
One of the largest beverage producers needed help monitoring, managing and increasing sales globally. The company is focusing on increasing demand for rapid insight from enterprise data to boost sales. Also the company is also focusing to reduce the total investment in data and analytics through more efficient infrastructure.
The company has used Bespoke data applications and tools (Spark, R and Hadoop) to enhance reporting through scorecard creation that provides advanced analytical insight.
- Increased beverage sales.
- Large-scale analyses of data sets that were previously too time intensive to acquire.
- Seamless access to data for business and IT users alike.
Microsoft CorporationCase Study: Microsoft helped Fast Shop to optimize multiple processes including pricing and customer engagement
Fast Shop, a leading Brazilian retailers specialized in providing in high-end consumer electronics and appliances. The company was facing problem in price setting as earlier pricing was set manually, and with 5,000 products for sale. SO the managers was only focusing on the products with the highest sales volumes and leave the rest to the judgment of sales staff.
The company has adopted Anzure analytics services, Azure data factory, Azure Machine Learning, Cortana Intelligent suite, Power BI, Azure SQL Data Warehouse for the restructuring
- Increase in sales
- Build 90% solution without IT help
DataikuCase Study: Dataiku helped showroomprive.com to anticipate and reduce customer churn rates
Showroomprive.com, a leading e-commerce player with over 20 million members in Europe. The ecommerce site has about 15 flash sales and over 2 million visitors per day. The company is focusing on reducing customer churn rates and improve customer loyalty based on individual purchase rates, detect clients with a high potential of no longer buying from the website.
The company used DSS to build a predictive analytics application that detects potential churners based on individual purchase rates. Also, the company use DSS solution to automate the integration and enrichment of a variety of data sources such as customer data, order and delivery data, web logs, etc.
- 77% accuracy in detecting potential churners
- Internalized data research and development
- In-house churn prediction system
MicrostrategyCase Study: Microstrategy helped Fanatics to enhance the performance on-demand
Fanatics, a largest online retailer of licensed sports apparel and merchandise, has started using MicroStrategy enterprise analytics platform to enhance the performance of the company on-demand. The company operates over 300 online and offline stores and powers the e-commerce business for all major professional sport leagues: the NFL, MLB, NHL, NBA, NASCAR, and PGA. Also, the company deployed a Hadoop distribution to manage and process unstructured log data, which will be analyzed using MicroStrategy enterprise analytics platform.
- Generated over 30 million orders a year
- Increase web traffic and clickstream data covering over 250 million web visits each year
Frequently Asked Questions
How will the Predictive Analytics Market perform in near future?The predictive analytics market size is expected to grow from USD 4.6 billion in 2017 to USD 12.4 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.17% during the forecast period. Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services.
What are the opportunities in the predictive analytics market?With the rise in touchpoint and the need for collecting data to understand consumer behavior, every touch by a consumer has become an important data point that can be processed to reveal user behavior. With the exponential rise in individual and organizational data, businesses are now deploying teams of data scientists and analysts to process the collected data. Another factor accelerating adoption is the revenue generating potential of predictive analytics. This is compelling firms to invest in predictive analytics.
What is the competitive landscape in the market?The predictive analytics ecosystem comprises vendors, such as Alteryx, Inc. (US), AgilOne (US), Angoss Software Corporation (Canada), Domino Data Lab (US), Dataiku (US), Exago, Inc. (US), Fair Isaac Corporation (FICO) (US), GoodData Corporation (US), International Business Machines (IBM) Corporation (US), Information Builders (US), Kognitio Ltd. (UK), KNIME.com AG (Switzerland), MicroStrategy, Inc. (US), Microsoft Corporation (US), NTT DATA Corporation (Japan), Oracle Corporation (US), Predixion Software (US), RapidMiner (US), QlikTech International (US), Sisense, Inc. (US), SAP SE (Germany), SAS Institute, Inc. (US), Tableau Software, Inc. (US), TIBCO Software, Inc. (US), and Teradata Corporation (US). The exponential growth in data volume is due to the expansion of businesses worldwide, which is driving the rise in data volumes and sources. The accumulation of big data in a single location has rapidly developed the evaluation capabilities of data science experts in every organization. Additionally, companies prefer to provide stand-alone solutions rather than combined solutions. This is eventually resulting in a rise in the number of big data analytics startups, which are driving noteworthy innovations.
What are the regulations that will impact the market?Predictive analytics leads to ad hoc analysis, which assists companies to have all workable solutions for their business specific questions and forecast past, present, and possible future predictive scenarios. In the current competitive business scenarios, companies need more than accurate predictive statements and reports from its predictive analytics. Companies now need more forward-looking, predictive insights that can help them shape impactful business strategy and improve the day-to-day decision-making in real time.
How are mergers and acquisitions evolving the market?July 2017, SAP collaborated with energy and services company Centrica to help their customers in managing assets and energy consumption on insights available through the IoT. February 2017, Oracle announced the expansion of its IoT portfolio with the introduction of 4 new cloud solutions to assist businesses to fully utilize the advantages of the digital supply chain. By applying advanced predictive analytics to devise signals, IoT applications can help in automating business processes and operations across the supply chain to enhance customer experience. March 2016, the company has extended their strategic partnership to offer combined capabilities of cloud analytics and big data to their users. This will help users to automate and simplify the decisions while attaining greater business insights for smarter business decisions.
What are the dynamics of the market?Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services. Globalization and economic growth are also playing major roles in driving greater data generation worldwide. Also, the rise in connected and integrated technologies has provided a platform to predictive analytics software vendors for leveraging this development and the unprecedented growth of the internet. Additionally, the eCommerce sector has modified the traditional shopping behavior of customers. Dedicated email campaigns, online/social media advertising, and cognitive analyzing of customers are the key enablers driving sales and increasing customers’ loyalty. With connected devices coming to the forefront, retailers are focusing on real-time analysis of customers’ shopping behavior and market basket analysis for analyzing consumers’ perception, which can be used for building tailor-made offers to increase customer retention. Similarly, with the rise in the global IoT analytics demand in the retail sector, the market is expected to have unprecedented growth opportunities for predictive analytics.