Customer Management Solutions Guide 2018

Q: How has the shift to omnichannel retailing affected traditional strategies for effective customer collaboration?

ZIMMERMANN: Although we have seen technology eliminate historical barriers to market entry and increase opportunity for direct-to-consumer engagement, product manufacturers must maintain a careful balance. On one hand, they must focus on nurturing longstanding relationships with retailers — who account for a significant proportion of their current business.

On the other hand, they need to explore new channels — such as social and e-commerce — to reinvent their marketplace relationships to unleash growth opportunities in an otherwise stagnant market.

Successful companies will build and collaborate with an ecosystem of suppliers, peers, distributors, startups, and retailers to respond to market and consumer demands at speed.

Sharing POS and consumer preference insight data are just some of the ways retailers and manufacturers can work together to avoid out-of-stock situations as well as define new and faster ways of delivering highly relevant products in-store at the right time and with the right buying incentives.

CAI: Regardless of size, companies can no longer afford to offer a single strategy for customer collaboration. They must be flexible, offer multiple options, and tailor strategies to the channel and the customer. Over the last 10 years, technology has been fully embraced and embedded into everyday life — both personally and professionally. Now, customers expect their vendors to provide services when and where they need it.

When it comes to collaboration, your sales rep in the field is no longer the main point of contact. But, in order to be effective, field reps must have access to better information from more sources. It’s critical for data to be intelligently filtered and shared with field sales reps in a way that is actionable and enables the front line to provide proactive services and solutions.

KEANE: We view the world from a retail execution point of view, so omnichannel for us means that manufacturers are splitting their channel coverage. Instead of dedicated field reps visiting every store every time, manufacturers are segmenting their stores, only visiting small-volume stores occasionally, and having the store manager order via web portals or having an outbound inside sales function taking orders.

The key is to have a single, unique view of your customer, so it’s best to use one order-taking solution. The field sales rep function starts to look more like a hybrid role with sales order-taking mixed with much more merchandising and consultative advice on how to sell more.

GOTTLIEB: The move to omnichannel has effectively multiplied the complexity around planning for shopper demand and balancing inventory cost with service level. Retailers and their CPG trading partners have gotten reasonably good at understanding inventory needs at the store level by looking at traditional metrics.

But the introduction of new, less predictable volume from e-commerce orders fulfilled in-store has created a need for the entire ecosystem to have better, more timely insights about store conditions — down to the specific item/fixture combination in order to avoid disappointing shoppers.

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Q: What stresses have these changes placed on existing planning and management tools?

CAI: I think omnichannel retailing has actually created opportunities for companies that are willing and able to embrace the strategy. Not only does it make it easier for customers, but it provides an opportunity for manufacturers and suppliers to gain insights and react quickly to changing market conditions.

Of course, omnichannel retailing comes with its challenges. Building out capabilities and continuously monitoring, revising, and improving customer collaboration methods won’t happen overnight. We’re in constant development to meet the changing needs of CPG organizations, but it’s imperative that we stay focused on providing retail execution tools that can support today’s needs. To be effective today, seamless connections across internal and external systems is a must.

GOTTLIEB: Existing tools are generally designed around the idea that units or dollars-sold-per-day-per-store is the ultimate measure that should be used to build an understanding of the shopper and store dynamics at an outlet level. The addition of e-commerce as a demand driver for each store means that demand can quickly deviate from historical patterns. The shopping time of day and day of week can change; plus, shoppers may move from self-shopping the store to a mix of delivery, click-and-collect, or all three.

This increasing complexity makes it ever more critical that manufacturers and retailers have a shared view of the store — one that is continuously updated, allowing them to enhance planning and collaboration.

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KEANE: With the rise of competing upstart and local brands, and consumer shifts to at-home delivery and online shopping, it’s more important than ever to ensure your retail/in-store strategy is brand-focused and refined. Brick-and-mortar retail is still relevant and innovating quickly. When consumers do shop retail, they expect product to be on the shelf and available.

Prevailing retailers are thinking more about the entire customer experience, meaning the consumer might first view the product in-store but then later decide to order online or vice versa. So the merchandising activities must tie into the manufacturer’s marketing activities that are going on independent of store sales and promotion. The product better be on-shelf and priced in line with any promotion, and the e-commerce portal needs to be consistent with the in-store experience.

ZIMMERMANN: Consumers have come to expect a smooth and seamless experience. This places pressure on CG companies to create a modern enterprise that can meet those expectations across digital and traditional channels.

CGs are looking into retail activity optimization, using the data gleaned to suggest the best course of action. Omnichannel insight data can be used to decide which retailers to visit and where the greatest opportunities for commercial impact lie. The result? Enhanced customer relationships and better, more effective use of the sales team’s time.

More than ever, it’s crucial that the data being used to inform selling strategies is high quality. Inaccurate or manipulated information threatens to compromise the strength of insights CGs can rely on when planning how best to operate and generate profitable sales.

Connected front-office solutions can pull and aggregate data from different omnichannel sources to enhance retail activity optimization. When it comes to planning and customer segmentation, CGs can better tailor POS activity thanks to the increasing granularity of data and shopper insights available.

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Q: What role can artificial intelligence play in improving the manufacturer-retailer relationship?

GOTTLIEB: AI will be the mechanism whereby the “baseline” of shared understanding is elevated. Today, many manufacturers and retailers have begun to use, at a minimum, a common set of data for item-unit and dollar sales. In the future, as AI is implemented and trusted more broadly, we will see this baseline being raised by additional metrics and insights that will not need to be debated but rather can be part of the foundation of collaboration.

Each retailer may move individually down this path and at a pace that makes sense for their culture and risk tolerance. But, we likely will see many of these new AI-driven insights making their way into the standard collaboration process in the coming months and years.

CAI: AI, machine learning, Internet of Things — these all can be used to improve the manufacturer-retailer relationship. These technologies can ensure the retailer has the right products available, at the right place and the right price for the consumer. They can also be used to increase sales, reduce out-of-stocks and decrease product returns, which creates a win-win for the manufacturer and retailer. 

To be effective, AI strategies cannot be “one size fits all.” For example, fast-moving products may require IoT to monitor stock and ensure availability at the shelf. Slower moving products can capitalize on AI to better understand conditions that will improve sales. In some cases, AI can provide accurate predictive orders to not only ensure availability, but to allow manufacturers and retailers to focus on faster-moving or higher-margin products. AI is going to be a real game changer for the industry.

ZIMMERMANN: AI has huge potential to strengthen the relationship between the manufacturer and retailer. By sharing accurate data and consumer insights, they can work together to train AI to deliver better insights, predict consumer preferences and behavior, enable new business models (such as subscription services) and, ultimately, strengthen brand image through enhanced personalization.

By taking the data held within companies (such as that gathered by social media listening, sentiment analysis and POS) and working to unlock the valuable insight that it holds, AI has the potential to not only get a better sense of consumer preferences, but to help reimagine and shape offerings available in-store. It also has the potential to bring “anywhere anytime” insights to CGs and retailers, helping them make quick decisions in real time. But, AI can only be optimized if people skills are augmented along with the technology.

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KEANE: CPG is a great use case for AI because of the huge multi-year data sets available. Every manufacturer has studies underway to optimize stuff like out-of-stock, perfect assortment, suggested order, and pricing. The challenge is making sure they’re executed in the field.

We see adherence ranging from 0% to 30%, meaning as many as 70% to 100% of sales reps ignore the algorithmic suggestions. So, we’re focused on adherence and compliance. You must take a huge data set and parse it so that relevant suggestions are presented in the context of the visit.

Likewise, the results of their action are fed back to the data lake to update the data set and train the AI engine. Algorithm wars? We are already seeing multiple algorithms being fed into the field, often offering conflicting advice. Which ones prevail and why? We think that “perfect visit orchestration” will become more prevalent in 2019. 

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To download the full report, including a comparison chart of 19 solution providers on the forefront of data and analytics, click on the link below.

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