The Dynamic Pricing War: Retailers Need Answers in the Face of Extreme Competition RESEARCH PARTNER

Transcription

1 The Dynamic Pricing War: Retailers Need Answers in the Face of Extreme Competition RESEARCH PARTNER

2 The Competitiveness of Retailers is being stifled by the likes of Amazon: A Closer Look at the Trends On average 8 out of 10 millennials rank price of the product as the leading influencer in the purchasing decision. Not surprisingly, most retailers have been giving consumers unseasonal price breaks and promotions to match up to the unwavering digital and off-price competition. Unfortunately for them, in spite of such short-term measures they are clueless on maximizing price and promotion effectiveness for consumers due to lack of focus on applying consumer and data science to pricing and promotions. 8 out of 10 millennials rank product price as the major purchasing decision. Lack of optimization and dynamic (real-time demand and supply data/insights) approaches in pricing, promotions and markdowns gets translated into lack of customer focus, loss in operating profit, gross margin and inventory turns. The loss accounted due to lack of pricing optimization is 2.6% for gross margin, 3.2% for operating profit, 4% for lost sales opportunity and 4.2% for inventory turns. A similar loss arising from absence of promotions and markdowns ranges between 3% to 3.4% for inventory turns and lost sales opportunity and 2.4% to 3.2% for gross margin and operating profit. What s pinching retailers the most or will have a long-standing effect on their profitability and base pricing strategies is big retailers, such as Amazon. More than 80% of retailers believe their maturity and competitiveness is at par with competitors but when it comes to Amazon, just over a third (36%) feel they are at par and more than half (57%) rank themselves behind Amazon. Clearly, Amazon is stealing the show when it comes to optimal pricing and promotions. Only close to 15% of retailers believe they are ahead in terms of maturity as compared to others (except Amazon). The Amazon effect clearly has an impact on their business and profitability.

3 Other observable trends that are shaping pricing strategy are: Real-time data gathering from various traditional (e.g. POS) and non-traditional sources (e.g. social, Wi-Fi, others) and the translation of such data into actionable insights across all retailers (both large and small). Need for deeper customer segmentation using a scientific approach so as to better understand the customer base at a local store level. Higher expectations of personalized pricing and offers from consumers that leads to deeper customer-retailer relationships and loyalty. Discount or off-price retail that has taken shape among 6 in 10 retailers, especially in the last two years. This trend has surprisingly been strong even in the luxury segment. 6 in 10 retailers have taken to discount or off-price retailing in the last 2 years.

4 Retailers Remain Disconnected from Optimal Pricing & Promotions 2.1. Challenges and pain points Despite having the knowledge that pricing and promotions are the key to remain competitive, retailers lack sufficient analytical techniques for optimal pricing and promotions. For large retailers, the top 2 challenges are (1) lack of actionable pricing & promotion analytics to drive profitable decisions and (2) inability to measure pricing or promotion effectiveness and financial impact. For mid-market retailers (with turnover above $1 bn), the main challenges faced are (1) increased vendor costs and ineffective vendor negotiations and (2) inability to connect pricing, promotions and markdown approach to a company strategy. It is quite evident that large and mid-market retailers are struggling to connect the dots between pricing and promotion effectiveness and profitability. Lack of actionable pricing & promotion analytics to drive profitable decisions. Inability to measure pricing or promotion effectiveness and financial impact.

5 2.2. Frequency of price changes: A more dynamic approach Nonetheless, the retailers have still tried to adapt themselves in this dynamic environment by resorting to frequent instore price-related changes which ranges from once a day to hardly ever. Large retailers resort to more frequent in-store price changes as compared to small and medium retailers. Almost a third (28%) resort to 2 times per week or even more frequent price changes per week. 1 in 3 retailers resort to price changes as often as 2 times per week. Large retailers are more agile to price changes for both in-store and online channels. 4 in 10 large retailers plan to increase price changes in the stores by frequency (how often a product changes prices) and volume (number of products with price changes). On the mid-market retailers front, they plan to spend more than large retailers on pricing & promotions in the coming year to ensure that they do not lose out to the likes of Amazon and large retailers. Most large and mid-market retailers acknowledge the need to spend more or the same amount as last year to remain competitive and meet their objectives. The Future: Capabilities 2.3. Planned capabilities to be built So, how do the retailers see themselves being equipped with sufficient capabilities to dynamically manage pricing & promotions? Is the use of advanced analytics for predictive and prescriptive pricing and promotions gaining momentum? In-depth competitive pricing trends analysis remains the top capability that both large and small retailers plan to use in the next 12 months. Competitive analysis requires deeper and faster category and SKU-level insights for competitive pricing, offers and markdowns.

6 However, forward-looking large retailers are also inclined towards adoption of capabilities such as AI and machine learning tools. Interestingly, mid-market retailers have emphasized that conducting price elasticity analysis is an effective tool for smarter-focused pricing, while larger retailers are prepared to go further and focus on the adoption of 1:1 promotions and offers. Large retailers will increase investment in areas such as localized and more granular pricing and promotion with a focus on dynamic pricing as well as system integration of pricing, promotion and markdowns in the next 12 months. Additionally, this means that compared to today, retailers will lay greater emphasis on more frequent, dynamic pricing, that will become more targeted through greater capabilities around customer segmentation and 1:1 capabilities. Nearly half (48%) of retailers are currently upgrading their promotions management/optimization capabilities or have planned replacement in the short run while 18% have identified the need for such an upgrade. Similarly, on the price management/optimization front, the numbers are 42% and 20% respectively. This shows that a fair majority of retailers are either upgrading or planning to upgrade their pricing and promotions management/ optimization capabilities. More than half (56%) of retailers have realized the importance of accurate demand forecasting and are upgrading their enterprise systems accordingly. Accurate demand forecasts based on predictive analytics and an understanding of product, customer and competitive elasticity will maximize financial impact of pricing and promotion activity of any retailer. Merchandise analytics is already in-place for 4 in 10 retailers indicating the increased usage of analytics in this field and another third are upgrading their merchandising analytics so that gut-feel approaches are replaced with consumer and data science. Close to 50% of retailers are currently upgrading their promotions management/optimization capabilities. 56% of retailers have realized the importance of accurate demand forecasting.

7 What Does the Future Hold: The Road Towards an Effective Pricing 3.1. Technology enablers Realizing the importance of technology in ensuring an effective pricing and promotion strategy, large retailers foresee higher spends in IT applications and infrastructure in the coming years. Spends on pricing, promotions & markdown solutions as % of IT budget is expected to increase in the next 4 years. The expected spend is much less for mid-market retailers compared to large retailers. Investment areas will not only focus on dynamic pricing but also on artificial intelligence (AI) and machine learning tools. Dynamic pricing has emerged a top 5 investment area for retailers and higher expectations of personalized pricing and offers from consumers is one of the leading trends shaping retail pricing today. Dynamic pricing or high frequency pricing when done right, should function in an automated process with exception management capabilities, incorporating machine learning science, retailer strategic and financial objectives, and pricing policies while accessing real-time competitive pricing. Dynamic pricing is one of the top 5 investment areas for retailers with both large and mid-market retailers increasing their spend on pricing and promotions. Other retailers will include format-based pricing, localization models and integration with other systems Willingness for frequent in-store price changes Till then, should price changes be reactive or proactive? Is a frequent price change to follow-suit an effective strategy for price optimization?

8 A wide majority of large and mid-market retailers are increasing their spend on pricing and promotions or keep the spend at a constant level. Moreover, at least a third are increasing price changes in terms of frequency and volume in the stores. 1 in 3 retailers have increased in-store frequency and volume of price change. More emphasis should be on proactive price change anticipating market demand shifts through proper planning and deep understanding of data via predictive demand analysis and consumer segmentation The road towards building capabilities In the long run, the framework to build a strong capability rests on adoption of deeper analytics and more responsive pricing, promotions and markdowns that are in line with market demand, consumer perceived ability to pay and competitive trends. Integrating systems and use of data from varied traditional and non-traditional sources to forecast demand and subsequent price changes is the key. Real-time data gathering (Big Data) is an important enabler in this effort as it helps in dynamic price comparison and anticipating market demand. Other important planned enablers in the retailer s arsenal includes merchandise analytics and markdown optimization. Store and SKU-level merchandising analytics and markdown optimization strategies help streamline and optimize merchandise buys and lifecycle pricing decisions for improved profitability.

9 Conclusion: Plugging the Gaps to Reap the Benefits of Optimal Pricing 4.1. Absence of optimal promotion & pricing, lost opportunities: The implications and their spill-over effect on consumers Lack/absence of efficient optimization and dynamic solutions lead to (1) lost sales opportunity, (2) shrinking operating profit and gross margin, (3) decreasing customer retention and loyalty. Such losses varies from 2.4% to 4.2% either in the form of gross margin or operating profit or lost sales opportunity or as inventory turns. Retailers are unable to absorb increase costs, and are forced to pass these increases on to the consumer. With the lack of dynamic pricing capabilities, retailers are unable to respond to market changes and competitive pressure at the frequency required. These dynamics result in retailers inability to offer competitive pricing to consumers on the products that matter most to them Focus on building deeper and more agile price and promotional capabilities For retailers, the immediate focus should be on building in-depth consumer and competitive pricing and promotional strategies that power a faster response to rapidly changing market conditions, consumer expectations and competitive pricing. Machine-learning science and analytical capabilities that are predictive and prescriptive at a granular level should be adopted to support pricing and promotional strategies in a dynamic environment. Once adopted, the process of optimal pricing and promotions will be a considerably less painful process. Retailers should focus more on leveraging machine learning science to build prescriptive analytical capabilities to empower dynamic price and promotion optimization and in-depth consumer segmentation

10 Appendix EKN 2016 Pricing survey demographics EKN s Adaptive Pricing, Promotions & Markdowns survey 2016, surveyed more than 100 retailers across US and Europe. Primary product segment Organization s annual revenue 30% 15% 15% 23% 17% Specialty Retail* Grocery, Food & Supermarket Apparel & Accessories Convenience and General Merchandise Others 26% 20% 24% 30% $200 million to $500 million $500 million to $1 billion $1 billion to $5 billion $5 billion + Business function Designation 14% 21% 16% 25% 24% IT/Technology Marketing (Includes Mobile, Social) Merchandising (MIS, Category Planning & Management, Pricing) Store Operations (Store, POS) Others 10% 30% 13% 8% 9% 20% 10% CXO SVP or EVP VP Sr. Director Director Sr. Manager Manager Figures are percentage of total respondents * Note: Specialty Retail includes Consumer Electronics; Do-it-yourself/Hardgoods stores; Sporting Goods and Discount stores Grocery, Food & Supermarket includes Department stores

11 Our research agenda is developed using inputs from the end user community and the end user community extensively reviews the research before it is published. This ensures that we inject a healthy dose of pragmatism into the research and recommendations. This includes input of what research topics to pursue, incorporating heavy practitioner input via interviews etc., and ensuring that the blend of research takeaways are oriented towards a real-world, practical application of insights with community sign-off. For more information, visit Revionics is a global leader in profit optimization services and solutions. Our unparalleled analytics and science serve as the backbone of omni-channel retailing to help performance-driven retailers execute profitable pricing, promotion, markdown, and space decisions with predictable business outcomes. The result: achieve speed to value and ROI, improve margins, drive top-line sales and respond faster with precision. Revionics SaaS-based model integrates analytics, technology and services to deliver an unmatched advantage for retailers Competitive Insights, Price Suite, Promotion Suite, Markdown Suite, and Space and Assortment Suite all from a predictive platform to drive long-term growth. Trusted by some of the most profitable retail brands, Revionics optimizes across 18+ million products, and more than 2.6+ billion item/store combinations are modeled weekly. Learn more at Disclaimer: EKN does not make any warranties, express or implied, including, without limitation, those of merchantability and fitness for a particular purpose. The information and opinions in research reports constitute judgments as at the date indicated and are subject to change without notice. The information provided is not intended as financial or investment advice and should not be relied upon as such. The information is not a substitute for independent professional advice before making any investment decisions. Copyright 2016 EKN Registered Office: 4 Middlebury Blvd. Randolph, NJ Ph: (973)