The scope for Generative AI to transform the retail value chain goes beyond forecasting and managing customer demand during major shopping seasons – although those are significant milestones in every organization’s retail calendar. Its real potential lies in tapping into generative capabilities reshaping the entire customer journey.

From sales records to customer preferences, retail brands are data goldmines. By fusing foundational language models with this wealth of information, retailers can harness Generative AI to craft personalized shopping experiences or improve business processes like never before:

• Customer support and assistants through improved LLM-based chatbots
• Intelligent search and summarization for inquiries and sales
• Consistent product descriptions generated through AI
• Synthetic inventory data generation to simulate supply chains
• Streamline the process of product development
• Label generation enhanced accuracy
• Personalized promotions through text and image generation

Building and operationalizing a bespoke solution using foundational AI models requires several components and enablers to be successful. Components for prompt engineering, dialog management, and information indexing are necessary to extract the best out of an LLM. When coupled with various NLP Accelerators such as document parsing, speech-to-text, and text embedding an end-to-end solution can be developed and deployed.

At Tiger Analytics, we’ve worked with various retail clients to elevate retail CX and work productivity and CRM with an AI/ML-driven customer data garden, streamlining and automating targeting models. Here are our observations

Streamlining Product Descriptions for Better Consistency and Cost Savings

Writing product descriptions for the entire catalog of products is a time-intensive and manual activity for most retailers. Add to this, the variations in consistency in terms of tone of writing style across different departments and countries make this a difficult problem to solve. Retailers need to ensure that the descriptions are relevant and concise to facilitate more conversions. They also need to keep the writing consistent across their e-commerce portals, campaigns, and digital content.

Generative AI can make this process smoother while being more cost-efficient. Customized LLMs can be trained to generate automated descriptions based on product attributes and specifications. Content can be standardized to the company’s style and tone for use across media. For retailers with an evolving product portfolio, this becomes a more scalable way to write and maintain product descriptions. Such a solution can be developed by fine-tuning a foundational LLM such as GPT, T5, or BART with annotated product data, product catalogs, and relevant SEO keywords. By incorporating human feedback, the descriptions can be further tailored to specific styles and needs.

Illustration for Consistent Product Description Solution

Customer Support with Better Understanding and Efficiency

The biggest problem with chatbots before LLMs was that they could not converse in natural language. This led to frustrating experiences for users who would eventually give up on using the bot. As many of the bots in the past were not well-linked with human agents, it led to low customer satisfaction and churn.

LLMs are a perfect solution to this problem. Their strength lies in generating natural language conversational text. They are also good at summarizing vast amounts of text into concise and understandable content. To develop a customer assist solution that works, retailers can deploy LLMs in key parts of the process:

1. Converting user speech to text
2. Summarizing the user query
3. Relaying summarized information to the user
4. Helping support agents query large amounts of information and generate concise responses.

LLMs need to be used in conjunction with components such as dialog management and work on top of issues, orders, and product data to deliver contextual responses to user queries. Due to the advanced context retention capabilities of LLMs, conversations can naturally progress with continuity, allowing for in-depth dialogue over an extended interaction and the context of the user’s query can be inferred clearly. This enhances customer interaction dramatically and can make the entire support process both effective and cost-efficient.

Illustration for Customer Assist Solution

Enhanced Sales and Customer Engagement with Virtual Shopping Assistant

Generative AI has the potential to personalize the customer journey across various touchpoints, creating a seamless and engaging experience. Imagine a shopper browsing through an online store, encountering suggestions that not only match their preferences but also anticipate their desires. The assistant doesn’t merely suggest; it understands, learns, and grows with the customer. By leveraging cross-category targeting and Next Best Action (NBA) strategies for existing customers, the assistant becomes a companion in the shopping adventure, guiding with insight and relevance.

Illustration for Virtual Shopping Assistant Solution

Beyond mere navigation and suggestions, the Virtual Shopping Assistant can also be leveraged as a smart chatbot to answer any product-related questions while browsing the website. To bring this vision to life, Generative AI can be customized and fine-tuned using detailed product catalogs, customer interaction data, and behavioral insights. By incorporating human feedback and integrating it with existing systems, the Virtual Shopping Assistant can be molded to reflect the retailer’s brand, tone, and values.

Synthetic Inventory Data Generation Boosting Agility and Insight

Managing inventory data is a complex and time-consuming task for retailers, often fraught with inconsistencies and challenges in scaling. Large Language Models (LLMs) can analyze extensive inventory data, identifying trends and patterns. This allows for the creation of realistic and relevant synthetic data without revealing sensitive information, providing both privacy and comprehensive testing capabilities.

With LLMs, retailers can gain control over the data generation process, enabling augmentation and diverse scenario creation. By fine-tuning LLMs with actual inventory data and incorporating human feedback, retailers can craft a system that aligns with their unique requirements. Generative AI’s ability to produce synthetic inventory data is not just a technological advancement; it’s a strategic asset that empowers retailers to be more agile, insightful, and effective.

Illustration for Synthetic Inventory Data Solution

Quick and Market-Aligned Product Generation

In the realm of retail, manual product development is a time-consuming and resource-intensive process. The challenges extend from heavy reliance on manual efforts by designers and stakeholders to the uncertainty in market success due to fluctuating customer demand, competition, and trends. The future state of product generation, however, offers transformative possibilities. By automating concept creation, design exploration, and prototyping, retailers can accelerate product development. This shift towards data-driven decision-making and key metrics identification further refines design choices and mitigates market risk.

Illustration for Product Generation Solution

The journey from concept to product can be streamlined through AI-driven stages such as generating product concepts, evaluating, refining, and iterating designs, and prototyping and testing. By leveraging customer data and market insights, retailers can create products that truly resonate with their audience. The ability to fine-tune the development process with actual market insights and human feedback aligns product creation with customer demand. This empowers retailers to be more innovative, efficient, and aligned with the ever-changing market landscape.

Generating Labels with Enhanced Accuracy, Brand Consistency, and Compliance

In the current retail landscape, generating labels is a labor-intensive process, marked by time-consuming efforts from graphic designers and product managers. Limited customization, error-prone procedures, and numerous iterations not only hinder efficiency but also pose risks to accuracy and compliance. This complexity impacts both time and flexibility, making label design a challenging task.

The future, however, presents an exciting transformation. Leveraging AI for rapid iterations, customization, and consistency opens doors to significant time and resource savings. The ability to offer scalability for large catalogs, ensure accuracy, maintain brand consistency, and comply with regulations is more than an efficiency gain; it’s a strategic advantage. By automating the design process and focusing on the creative aspects of label design, retailers can elevate their brand’s identity and engage with their audience in a more meaningful way.

Illustration for Generating Labels Solution

Personalized Promotion for Enhanced Customer Engagement

Creating personalized promotions has traditionally been a manual, error-prone process. Manual analysis and segmentation of customer data can lead to limited insights, inefficient promotion design, and static promotions that lack relevance. The challenges in uncovering subtle customer preferences make it difficult to deliver truly personalized experiences.

The future state of personalized promotion, driven by AI, offers a transformative approach. Automated customer segmentation, real-time personalization, and adaptive promotions bring accuracy and dynamism. This shift not only improves efficiency and maximizes ROI but also ensures a seamless and cohesive customer experience throughout the shopping journey. By focusing on real-time insights and multichannel personalization, retailers can connect with customers in more meaningful ways, enhancing engagement and loyalty.

Illustration for Personalized Promotion Solution

The emergence of Generative AI in retail signals a transformative era, offering immense potential to enhance every aspect of the retail value cycle. From creating more engaging customer experiences to optimizing supply chain management, the applications are vast and varied. Retail leaders who leverage these technologies can significantly improve operational efficiencies, personalize customer interactions, and stay agile in a dynamically evolving market. By harnessing the power of Generative AI, retailers are not just adapting to current trends; they are actively shaping the future of retail, paving the way for innovative approaches and sustainable growth in an increasingly digital world. Now is a pivotal moment for industry leaders to explore and invest in these advanced capabilities, ensuring they remain at the forefront of retail innovation and excellence.

The post Beyond Bargains: 7 Powerful Ways Retail Leaders Can Use Generative AI to Level Up Their Retail Value Cycle appeared first on Tiger Analytics.

In our view, smart use of data and CPG analytics – ranging from relatively simple exploratory data analysis to advanced DS/ML/AI models that deliver actionable insights in five key areas can help CPG manufacturers improve their growth and competitiveness: E-commerce, Trade Spend Effectiveness, Pricing, Marketing Effectiveness, and Responsive Manufacturing & Supply Chain.

In a series of posts over the next few months, starting with this overview, CPG industry practitioners from Tiger Analytics will talk about current best practices in data management and analytics around these five areas:

1. E-commerce – For most CPG manufacturers, a significant part of revenue growth (if not all) in the next few years is expected to stronge from e-commerce. Surprisingly, this is also a channel that is not very well covered strongy the traditional data providers. Pioneers in the industry are adopting unique approaches to acquire, integrate & analyze e-commerce data that puts them ahead of the game. If you are wondering about Direct-to-Consumer (DTC), see point 4 below.

2. Trade Spend Effectiveness – Various studies conducted over time show that return on trade spend, which is either the first or second highest spend often running into billions of dollars, is negative for most of the CPG manufacturers in the industry. While robust trade promotions management (operational aspects of defining promotional calendar, promo execution & reimbursements) is in place across the board, analyzing the effectiveness of spend and establishing ‘true incrementality’ of trade spends is an area of big concern. Here again, CPGs that win are going beyond toolsets offering a siloed view of incremental volumes (i.e. just one retailer at a time) to get a full view of effectiveness based on comprehensive data and advanced analytics.

3. Pricing – While promotions look at the effect of temporary trade events, getting everyday prices right is equally important, if not more, since a significant portion of CPG manufacturer trade spend goes into maintaining long-term price gaps warranted by competition, or by retail partner demands. While most CPG manufacturers make pricing decisions at an item/item group X channel level, winning CPGs are making these decisions at a much more granular level, here again taking advantage of data & analytics.

4. Marketing Effectiveness – Marketing spend, which is a close counterpart of trade spend is another top spend item on a CPG manufacturer’s P&L. Two key sources of data & insights have been found to improve effectiveness:

• Consumer insights generated using data from consumer panels, shopper cards, social media, brand page user registrations, and other Direct-To-Consumer* initiatives help get a clear understanding of consumer preferences and decision hierarchies

• Insights around traditional and digital media channels (in the form of transparent media mix models) and their impact on short and long-term brand objectives

Of all areas, this is probably the most investment intensive from a data & analytic infrastructure perspective when CPG manufacturers want to go beyond a siloed view (often lamented as coming from agency delivered black-box models, or from the ‘walled gardens’ of the digital world). However, the pay-offs for initiatives are worth the effort, especially for larger brands with higher marketing spends to deliver returns on.

5. Responsive Manufacturing & Supply Chain – compared to other areas, manufacturing & supply chain functions of CPGs have the need to plan over multiple time horizons, across all of which data and analytics play an important role.

• Near-term (up-to 3months): analyzing out-of-stock and fill rate for maintaining retail partner service levels, and impact of transportation partner & lane decisions are relevant in this time horizon.
• Mid-term (>3 to 12/18months): understanding impact of promotion plans on shipment volumes over and above the baseline demand picture provided by the Demand Planning function is important in this timeframe, to plan and adjust mid-range production schedules without much of incremental capacity.
• Long-term (>12/18months – esp. for manufacturing plant and warehouse capacity planning): SKU rationalization – a touchy topic becomes feasible to address in this timeframe due to the amount of internal and external change management it takes to move on this, more than the time taken for analytics.

Interestingly, this is also a space that is witnessing a higher use of Robotics (automated inventory management and warehousing).

While it may appear addressing all the five areas may require significant upfront investments in integrated data environments (DSRs), our experience shows having a clear focus on leveraging analytics to generate specific, actionable insights could help realize significant business value immediately, even on current state data & analytics environments – wherever in the maturity curve they are.

Stay tuned to hear more from us in this series.

Note* Direct-to-consumer initiatives of most CPG manufacturers are often more valuable as a source of augmenting rich consumer insights than being a significant channel of sales volume. Exceptions being brands with significant direct-to-consumer sales, such as lifestyle/sportswear brands and health-beauty-cosmetics manufacturers extending their consumer reach through their own salons.

The post CPG Analytics of Today: What Are the Top 5 Current Priorities? appeared first on Tiger Analytics.

Surprisingly, for such a large head of expense (only COGS is higher), there still remain issues around monitoring spend effectiveness and subsequent optimization. Admittedly, a lot of work has been done in this space – there is better data, courtesy syndicated data providers and better collaboration between retailers and CPG companies, with some retailers sharing POS level data. There are also a number of packaged solutions that help streamline the operationalization of promotional activities. But in all this, a lot of emphasis has been placed on running promotions efficiently and collecting data but it has not always translated into effectiveness, with most spend still resulting in a negative return.

(As in any industry, there are leaders and laggards in the adoption of trade promotion practices but the general impression across the board is that effectiveness can be improved).

More importantly, even for relatively mature clients, our client conversations reveal some interesting gaps in this space:

Analysis Needs to be Granular Enough to Drive Action:

For example, price promotion optimization needs a good understanding of the elasticities at a product/retailer/region level. A lot of the existing analysis that we have seen, however, stays at the national level, which cannot facilitate tangible action.

For a client of ours, we executed a trade promotion optimization program focused on understanding and using price elasticities. This was done for multiple line extensions and we controlled for distribution, seasonality, substitute/complement pricing and media support to obtain robust price elasticity estimates.

With the above, we enabled simulation scenarios that provided a clear way to ‘move’ spend around, without changing the overall amount (which is a larger change management issue). This was the first time that the marketing team obtained the linkage between price and revenue at a line extension channel/retailer/region level with a clear view of potential revenue upside.

Analysis Needs to Consider the Broader Impact of Trade Promotions:

In measuring promotional lift, same-store impact due to a promotion is easy – establishing baseline sales, determining cannibalization within the brand portfolio are fairly well-known aspects. Syndicated data, for instance, provides a directly available ‘base’ sale and promoted sale number. But promotions viewed in isolation for a retailer ignores a huge behavioral aspect of consumers – they might be happy to move to another retailer in the vicinity. A cross-retailer view enables a more realistic picture of the impact.

A Client of Ours had an Interesting Problem:

They were measuring retailer level impact of promotions – including base, promotions, cannibalization within retailer, impact of competition activity for that retailer – and almost every promotion seemed to have a positive impact on sales. However, when aggregated across retailers for a larger timeframe, the results were obviously lower. So, there was a ‘cancellation’ effect in play which seemed logical but they had no data/model to support this.

We executed a program focused on obtaining a comprehensive picture of the impact of promotions – the most important part being cross-retailer cannibalization impact. This was done for multiple promoted product groups (PPGs) and we blended trade area data with retailer PoS data to get a retailer vs rest-of-market (RoM) view. One of the key challenges that we addressed in data preparation was separating out everyday pricing vs promotional effects from available store x item x week level data.

Using Trade Promotions and Sales Data:

This allowed us to analyze responses to CPG funded promotions for a retailer as well as RoM, and we estimated net impact of promotions on consumption volume as well as sales revenue.

We built a scenario planner for use by account executives to design promotions with higher net lift, considering the impact of cannibalization. The business ultimately got a reliable and repeatable methodology for performing trade impact analysis at the category x retailer-vs-rest-of-market (compared to just one retailer at a time) for an accurate estimate of net lift. This solution is being evaluated on trade spends of USD 20MM+ accounting for revenues of revenues USD 250MM+, with a plan to rapidly scale up.

These are just two examples of optimizing trade promotions, even when companies are relatively mature in their trade promotion management practices.

The post Trade Promotions and CPG Analytics: How to Unlock True Success appeared first on Tiger Analytics.

Given the relatively weak presence of large retail chains in emerging markets (as opposed to the developed economies, where they are either dominant or at least have a much more formidable presence), the issue of assessing, on a continuous basis, the performance of the sales teams and taking action to maintain or improve the same is of paramount importance. In fact, not just in emerging markets – a lot of these apply even in developed markets where the trend is toward CPGs driving store-level decisions in collaboration with retail partners. This is a clear shift from a past that mostly dealt at a channel x national level.

Despite the critical role of the sales force, we have observed that outlet level/salesperson level measurement & insights and using that to improve performance and effectiveness isn’t that common or consistent, resulting in lost opportunities (visible manifestations being empty shelves, obsolete stock, and lack of ‘right’ choices for consumers at the point of consumption – all with associated financial impact for the brands).

In our experience, making a simple start by:

a) Tying in data across three specific areas (detailed below) – all within sales, and analyzing them to understand key levers that impact performance (vs hundreds of metrics/reports), and

b) Leveraging predictive analytics to discover headroom for growth at the right level of granularity to be actionable, often delivers a disproportionate impact vs effort spent.

Sales leaders already look at the following:

1. Sales force effort – attendance, hours spent, outlets visited, etc.

2. Compliance tracking – Right prices, promotion compliance, digital recording of orders

3. Results – achievement of targets, brand coverage, effective distribution

While a lot of metrics and reports are created out of such data to help make decisions on a regular basis, there’s a lot more that can be done with the same data and infrastructure to better assess the pain points and help the teams do an even better job.

1. Optimizing the Sales Leadership’s Bandwidth and Use it to Create Maximum Impact

The first and possibly the most powerful way to help the sales team is by helping the sales leadership prioritize efforts and budgets toward the biggest and most easily/quickly addressable problems. This can be done through analytic models that establish a link between performance (either sales goal based or a customized composite performance metric) to the most important drivers of that performance from the hundreds of activity metrics that are tracked.

The models can then score prospectively and identify potential locations that need attention to avoid slip-ups in achieving overall goals. This has been found to be immensely useful as it helps leadership adjust the right levers to drive impact on sales.

2. Help Sell the Right Products to each Outlet

A product/SKU recommendation engine integrated with outlet-order taking mobile app, built using AI-based advanced algorithms will help streamline the selling process and free up the salesperson’s time to focus on priority items such as selling focus SKUs as well as merchandising opportunities.

3. Assess Outlet Level Potential for Purchase, Stocking and Merchandising

Each retailer outlet has a unique capacity and appetite for stocking and selling different product categories as well as an affinity for certain types of promotions. Understanding this through their purchase history as well as those of similar retailers in the neighborhood, AI-based algorithms can provide real-time recommendations on the optimal type and amount of selling effort for each outlet.

4. Prioritize/Sequence Outlet Visits through the Day and Week

Different outlets and their handlers/managers, especially in the emerging markets, have different times of the day and days of the week when they are most open to salesmen visits as well as their sales spiels. Optimization algorithms can be developed to generate the most productive weekly/fortnightly route plan for the sales team to get the biggest bang for their efforts.

5. Design and Implement Targeted Consumer and Trade Offers (CO/TO) Based on Retailer Characteristics

CPG companies frequently roll out different promotions aimed at either boosting secondary sales (retailer purchases) or the consumer offtake (tertiary sales). These typically tend to be blanket promotions applicable to most outlets that their distributors cover, with only the occasional differentiation between wholesale and retail.

In reality, there are many different retailer groups out there looking for different incentives/rewards from the manufacturers. Understanding what these are and using sophisticated retailer segmentation solutions combined with AI-generated smart custom trade/consumer promotion plans can help give the retailers and consumers what they want, detect and prevent misuse of trade discounts, and improve the ROI on promotion budgets.

These are just a few ways to help CPG sales teams, but a great starting point, with the data already available with the organizations.

Does your sales force have the power of such insights helping them drive growth?

The post Unleash the Force of Sales: How CPG Teams Can Thrive appeared first on Tiger Analytics.

The clear, permanent shift towards digitally driven consumption is turning up the heat on retail, but for CPG manufacturers it represents an opportunity – at least till now, should we say? Ecommerce platforms are in fact fast becoming the main growth area for CPG companies, across geographies. (For some interesting statistics, we recommend reading this insightful article from Nielsen).

To win, CPG manufacturers adopt one of the following eCommerce models:

1. Partnerships with pure-play online retailers, of which Amazon has been a prime example (pun!?). While it is predominantly online, it is no more pure-play online only.

2. Online channels of traditional brick-and-mortar players (the walmart.coms of the world)

3. Direct-to-Consumer platforms with a content + commerce angle. Till now, this model is used more for content sharing with consumers than commerce – less than 1% sales across most categories, but this could change as CPG supply chains adapt.

Across all three of the above, especially on the commerce part, levers to win (as articulated in the pictorial below) are not very different from what worked in the pure offline world. Given our experience is mostly around delivering insights through data & analytics, the rest of this article is focused on that aspect.

Importance of eCommerce Data for CPG Manufacturers

In the offline world, CPG manufacturers get a broader market picture (volume/value sales, brand shares etc.) relying on data from syndicated providers (largely Nielsen & IRI) than having to pipe in PoS data* from every major retailer. We often hear in discussions that for the ecommerce side of the market, syndicated data sources are yet to fully mature to provide a reliable market view covering all relevant ecommerce players across the range of key categories. Given this context, an absence of any proactive data initiative by a CPG manufacturer will make it difficult to understand its brand/category growth drivers at the right level of granularity and use that to improve share. Striking the right data sharing partnerships with relevant ecommerce partners to get sales data in place and subsequently leveraging that for a range of insights-driven in-channel / cross-channel decisions is a high priority area for many top CPG companies.

[* Of course, CPGs leverage PoS as well, for account specific deep-dive insights and service level improvements, another trend that’s accelerated from around 2010 with the advent of affordable solutions to store and analyze data]

In an ideal state, marketing data – across traditional as well digital marketing channels, are brought in to comprehensively cover the ‘stimuli’ side, while sales data – shipments as well as data on consumption through ecommerce, PoS, syndicated, are brought in for a holistic picture of the ‘response’. Then there are others such as panel data, shopper cards etc. However, given such a large scope could take many years to materialize, CPGs often divide and conquer. When it comes to bringing large quantities of data in-house, or on to a platform managed by a partner on behalf of the CPG manufacturer, the scope of data that is brought in as well as the underlying technology infrastructure scales asymmetrically across functional areas, depending on:

1. Cost and ease of availability of data,

2. Ease of bringing the data on to internal platforms,

3. Utility of such data in relevant areas of decision making, and

4. Business value potential

These together drive the allocation of funds from the functions. In such a model, we have often seen acquiring data from ecommerce partners getting a higher priority over bringing highly granular data around digital consumer journeys which could be understood to a reasonable extent via agency provided solutions rather than bringing all marketing data in, on priority.

[Please note this is more of a sequencing decision than a substitute forever. Some of the respected global CPGs we work with already have a good hold on the marketing side of the data and are now moving to conquer the eCommerce side; but if you are just about starting on the data journey, the sequencing would be different, in our point of view. If not immediately, very soon, bringing comprehensive digital consumer journey data will become highly relevant to bring in rather than only rely on external inputs – if a CPG manufacturer’s aspiration is to be a category leader driven by differentiated insights. That’s another topic for a different day: back to ecommerce now]

Data Management

When it comes to building a solid data foundation for ecommerce, it is essential for CPGs to have the following covered:

Scope of data: Building out the overall strategy for data management requires scoping out data needed to align with business objective and availability. Data acquired from partner retailer includes ecommerce Point of Sales data, content & online behavior, and search terms. Internal firm data could be integrated with aggregated sales to provide a holistic picture. It could include promotion calendar, shipments specific to ecommerce, priority search terms, pricing strategy (ceiling/floor) etc.

Building robust data pipeline & quality control: It is crucial to build a scalable data pipeline. This includes collecting data from different sources, storing the data – including in-memory processing & storage, in an optimal way so that the data mining is effective and less time-consuming.

Data cataloging: Creating standardized metadata and ability to look up with master data is another critical element to ensure roll-up from individual components of data from various sources/geographies.

Governance: Establishing processes for data visibility (to users), data lifecycle management, aligned data aggregation rules vs arbitrary, and data quality monitoring to ensure effective data management

Covering these will ensure trusted & timely delivery of data to users. In summary, it is critical for CPG manufacturers to build expertise in eCommerce data management to ensure adequate data assets are available for the downstream analytics – whether such analyses are heavyweight predictive analytics by data scientists or lightweight DIY analyses by business users.

eCommerce Analytics

After* successfully building the data foundation, the next step is to arm the business with precise insights that drive differentiated execution. Yes, but in which areas? As outlined above, the other three critical pieces to win in eCommerce are Content& Communications, (R)etailer Partnerships, and Pricing & Promotions.

[*While the mention of analytics ‘after’ data may indicate the progress from data to analytics is very linear, most analytic themes are often tested for value using snippets of data even as the data foundation is taking shape and are quickly scaled across customer teams when a critical mass of data is in place.]

Analytics Around Content

Analytics is useful to optimize content and syndicate it across e-retailers. Causation analysis of product sales with product information is critical to not only optimize the content but also adapt to ever-changing needs for digital engagement. With deeper data from partners, the impact of product imagery and messaging on ecommerce sales is doable too, but not that prevalent as yet.

Partner-specific Analytics

A long tail of products is commonly observed in ecommerce where shelf space is unlimited. One of the struggles for CPG manufacturers is to understand their most profitable products and optimize its assortment for adequate coverage of consumer segments and price points for each retail partner.

If some form of consumer profile data (even aggregated) is available from the partner, that’s of great help. Even if not, classic price-ladders and blended data analyses with online purchase behavior from consumer panel providing a view of the preferences help make a start.

Another big area here is around service levels on shipments against ecommerce orders – which is as critical for ecommerce as in the off-line world. A quote seen in a recent article from BCG best summarizes this – “Stockouts, bad enough in traditional retail, can be deadly to an online seller”. It couldn’t have been said better. We recommend reading it for a detailed treatment of how CPG supply chains are adapting in the context of the three different ecommerce models.

Price & Promotions

Effective promotions – the online equivalent of traditional trade promotions, however it is named and accounted for, is highly critical in ecommerce as well for multiple reasons. It’s often a top spend item for CPG brands. In addition, pricing and promotions are highly visible for shoppers and comparison engines alike. Hence, it pays (quite literally!) to understand ROI through detailed impact attribution of promotional activity – what worked, where, when and what’s the true incrementality vs share shift. With good quality ecommerce partner data, this is an area that can be addressed with sufficient confidence. Even if only marketing spends are available and not detailed consumer journeys, it is still possible to get a good picture of promotional impact.

Consumer Digital Journey

This, of course, is not about ecommerce alone, but an integrated view of data from both ecommerce and marketing. Depending on the depth of the data, it is quite possible to build the equivalent of a Market/Media Mix Analysis specifically focused on consumption through the ecommerce channel; or get a complete view of the digital consumer journey replete with attribution analysis.

Of course, for such deeper analysis, data on offline + online media consumption as well as category consumption is required, which comes from specialized players, at additional cost. This approach helps get a comprehensive omnichannel consumption view of the consumer than having to draw artificial offline/online boundaries. Given that, such intensive data acquisition decisions are usually not taken alone by ecommerce teams, but with a total consumption perspective in conjunction with brand marketing and consumer market insights (CMI) teams.

Conclusion

For a USD 10 billion-sized CPG business with a 1.5% annual growth, if most of that growth in next few years is expected to come from ecommerce, that means a USD 500 million business opportunity over the course of 3-4 years. No wonder then that this space attracts much of leadership attention. As opposed to a general approach of evaluating ROI on individual initiatives, when it comes to eCommerce, looking at this bigger picture has helped many in the industry to quickly move from planning to execution mode.

The post 2024 CPG E-Commerce Trends: AI and Personalization Take Center Stage appeared first on Tiger Analytics.