Introduction: AI and advanced analytics can transform large amounts of data accumulated by consumer goods companies into effective business insights, resulting in more than 10% revenue growth.
Valuable business insights are buried in the massive data of the brand. Through large-scale use of artificial intelligence and advanced analytics, consumer goods companies can turn this data into business insights and then share them across the organization—from product design to supply chain to marketing and sales.
In the market competition for the consumer goods industry, large consumer goods companies have recently been embattled, with challenges ranging from agile niche brands to traditional retailers and online retailers. Data advantages and direct interaction with consumers to promote their own brands or substitute brands. However, large consumer goods companies have multiple ways to fight back. One is to use artificial intelligence and advanced analytics to turn their data into valuable business insights (see "The rise of niche consumption, how do big brands fight back?").
In order to understand the value, impact and challenges of using artificial intelligence and advanced analytics by consumer goods companies, BCG and Google jointly conducted a study. During the research, we interviewed executives from 25 large and medium-sized consumer goods companies and five niche brands, as well as about 100 industry experts around the world. We have found that through the large-scale use of artificial intelligence and advanced analytics, consumer goods companies can make more forward-looking demand forecasts, product portfolios that better match local needs, and provide consumers with personalized services and experiences that enhance marketing and Promote ROI and shorten the innovation cycle to achieve revenue growth of over 10%.
But for most large consumer goods companies, it is still difficult to fully release this value. While almost all of the companies surveyed have begun experimenting with artificial intelligence and advanced analytics in their core business, no single company has promoted even one application. They enumerate many organizational barriers, such as senior management's cautious support, poor data management, lack of taxonomy (ie, agreed data frameworks), team fragmentation, and failure to adequately predict artificial intelligence and advanced analysis. The impact of jobs and work styles.
It is true that companies that want to benefit from tools such as artificial intelligence and advanced analytics require continuous and concerted efforts to overcome all kinds of difficulties. consumer goodsCompanies should also be precise, focusing on three to five high-priority areas, rather than being fully rolled out.
Let artificial intelligence and advanced analysis fall
Consumer goods companies have more opportunities to access massive amounts of information, from traditional enterprise data (from financial and operational departments) to consumer data (especially online consumption) Behavior), then to partner data (typically through workgroups, retailers, business insight partners, or others), and even data generated by sensors and the Internet of Things (IoT). However, so far, consumer goods companies have not regarded these data as a strategic asset that needs to be protected and cultivated, nor have they been used to create practical benefits for the company.
With artificial intelligence and advanced analytics, companies can get viable business insights from this data. The most notable applications of artificial intelligence and advanced analytics are predictions, such as predicting the level of demand for new products, the effectiveness of marketing campaigns, and the burgeoning new consumer trends.
After research, we identified the application scenarios in which enterprises can use artificial intelligence and advanced analytics to promote business development. There are about 30 kinds of applications involving all functions of consumer goods companies, from marketing and insight to operations. , sales and support departments. These scenarios can also be used to promote the development of innovative services such as personalized assistants and recommendation engines.
We selected ten of these 30 application scenarios, representing the greatest growth opportunities that artificial intelligence and advanced analytics can bring to consumer goods companies. If it is promoted on a large scale, sales are expected to increase by more than 10% (see Figure 1).
1. Demand forecasting for existing and new products based on item inventory (SKU) and regional differences
2. Assess the return on investment to predict the impact of advertising and promotional expenses on sales
3. Data-driven promotions, identifying suitable retail outlets or point-of-sales, using appropriate promotional campaigns based on storefront levels to maximize market share
4. Optimize product matching according to the operation of each store
5. Trend forecast for product development
6. Reduce development and testing time (via computer simulation)
7. Flexible, localized, personalized pricing and promotions
8. Precision Marketing
9. Personalized consumer interaction
10. Artificial Intelligence-Driven Diagnostics and Recommendation Services
It is worth noting that these applications (including trend predictions) are most relevant to industries with the following characteristics: short product launch cycles (eg cosmetics industry); dynamic pricing and Industries that are frequently negotiated with retailers for effective promotion (relatively rare in the food and beverage industry).
While it is relatively easy to identify the most effective artificial intelligence and advanced analytics applications, deploying these applications across the organization is a task that most consumer goods companies cannot. Of the 30 consumer goods companies we studied, all have begun to implement at least one artificial intelligence and advanced analytics applications, half of which have begun testing four or more applications (see Figure 2). But no company can promote even one application across the organization.
Massive promotion, obstacles
Most of the consumer product company executives interviewed said: Expanding the scale of application of artificial intelligence and advanced analytics technology, and ensuring that it is internally accepted as an executive A key topic that we are currently exploring. In addition, they also listed some challenges. Even promoting only one application is very difficult because it requires a lot of investment and management cooperation across the organization.
Businesses often need to build small proof of concept (PoC) in specific countries or for specific applications for specific brands. However, if companies want to deploy proof-of-concept on a large scale, they often need multiple efforts: developing artificial intelligence and advanced analytics, and when they are strong enough, they can be deployed globally across the enterprise; developing data to consolidate data quality, Unify cross-country, cross-brand taxonomy; existing IT systems may become redundant due to the inability to support new artificial intelligence and advanced analytics, or current IT systems must be significantly adjusted to provide or receive applications The data in .
Other areas of concern include existing business processes, management rules, and job descriptions, as artificial intelligence and advanced analytics will change the current decision-making process, automate manual tasks and calculations, and change a large number of employees. And the daily duties of the manager. Finally, talent and skills are also issues that companies cannot ignore because building and maintaining artificial intelligence and advanced analysis shouldUse a strong talent pool (data scientists, data engineers, and data analysts). Consumer goods companies need to at least improve their ability in this area to avoid relying entirely on third-party suppliers.
In view of the above difficulties, in order to effectively promote artificial intelligence and advanced analysis in consumer goods companies, the following six obvious obstacles must be removed:
1 lack of foresight
The benefits of artificial intelligence and advanced analytics can be fully assessed, and the corresponding rewards are not attractive enough; executives are not adequately trained and limit their investment willingness.
2 The primary and secondary points are not divided
This will lead to the “prototype verification explosion”, which will make the enterprise's efforts go to waste. The company conducted a number of small tests with different suppliers, but did not perform any follow-up operations, nor did it put the necessary efforts into industrialization, scale-up and promotion.
3 Talent gap
Identifying, recruiting, and retaining talent (data scientists, data engineers, and data analysts, etc.) is no easy task, resulting in over-reliance on external suppliers. It is difficult to control the implementation. At the same time, companies have repeatedly tried to develop local talent, but often lack the clustering effect.
4 Insufficient data management
The company's data management, data quality or data ownership process is absent, and there is no unified (cross-departmental, cross-country) data classification to promote artificial intelligence and advanced Large-scale application of analysis.
5 Underestimate the influence
Such companies misjudged the level of investment needed to change management and develop relevant skills. Organizations cannot fully predict the impact of artificial intelligence and advanced analytics on existing business processes, decision processes, management routines, and the day-to-day work and skills required of employees.
6 Insufficient consideration of market differences
Companies often overlook the differences in digital ecosystems, data availability, channel characteristics, and supplier capabilities in different markets. Companies are also unaware that market needs, priorities and constraints vary in different markets.