The pandemic impacted businesses in various ways. Within the consumer-packaged goods (CPG) industry, for instance, some companies struggled to survive while others experienced never-before-seen spikes in consumer demand.
The transition from responding to the pandemic to recovering and navigating the path forward means that CPG companies must manage several priorities simultaneously:
- Keeping pace with dynamic consumer preferences
- Identifying incremental growth opportunities
- Becoming more agile to pursue opportunities
To be successful, CPGs and retailers must remain consumer-centric and analytics-driven. And research shows that’s what’s happening. Forrester found that, on average, organizations use five BI solutions to extract corporate data from various enterprise tools (e.g., CRM, ERP, financial planning). According to Valuates Reports’ latest research, the global business intelligence and analytics software market size is projected to grow nearly $10 billion ($23.94 billion to $33.77 billion) between 2020 and 2026, at a CAGR of 5.9% during the forecast period.
Per the research, some of the significant factors driving BI and analytics software growth are a growing focus on digital transformation, rising investments in analytics, rising demand for dashboards for data visualization, increased adoption of cloud technologies, and increased data generation.
Traditional BI Dashboards Don’t Tell the Whole Story
While BI tools have been the go-to for enterprises, the fact remains that many data initiatives fail to reach their potential because of the massive amount of spreadsheets and reports data scientists must read through and decipher before presenting actionable takeaways for the business. The problem is that new trends emerge quickly – especially in CPG and retail.
Waiting for teams to present their findings means lost revenue opportunities.
Companies are gaining an edge (and where VARs, systems integrators and MSPs can help their clients) by augmenting BI platforms with embedded, no-code AI that accelerate data understanding and informed decision-making. Augmented analytics comprises machine learning and various artificial intelligence technologies such as natural language generation (NLG) to assist with data preparation, insight generation and explanation to supplement how people explore and analyze data in analytics and BI platforms.
Companies like Arria NLG, for example, enhance Power BI dashboard users with visuals supported by data explanations in natural language. These intelligent narratives deliver clear, written summaries of data insights to viewers of the dashboards throughout the organization, enabling actionable insights with user-configurable, out-of-the-box descriptions.
In a recent Power BI blog, Microsoft Power BI Program Manager Jeroen ter Heerdt adds, “Arria’s narrative capabilities support your dashboard’s visuals – and describe the underlying data – with expertly written short-form summaries or long-form reports. [It enables you to] drill down into all your dashboard’s underlying data to tap into insights you might otherwise miss.”
NLG Takes Analytics Beyond Insights
NLG is the component of augmented analytics that translates a machine’s findings into words and phrases humans can understand. Specifically, NLG focuses on data analysis output. For example, when a system finds that sales are down in a particular category, NLG reveals that “Sales in Category A declined by 23 percent over the previous month.”
NLG is a vital partner to machine learning because it enables the average, non-technical person to understand what’s occurring in an enterprise’s growing volume of data. This democratization of data is not just about effectively communicating data trends; it’s about transforming intangible algorithms into real answers presented in a way humans can understand.
That said, the value of natural language isn’t solely limited to generating insights. For example, some augmented analytics platforms apply natural language to their search functions so users can ask questions like “What were sales in August 2020 by category?” and receive an answer in the form of a visualization.
How Augmented Analytics Can Help Pharma CPGs
The pandemic severely disrupted pharma supply chains, leading to 118 FDA-reported drug shortages in August 2020. Of the 118 drugs, 55 were related to an increase in demand, 16 to API (active pharmaceutical ingredients) insufficiency and 8 to manufacturing or shipping delays.
Without access to data-driven insights and actionable intelligence, pharmaceutical producers have shown their inability to react fast enough to secure the needed supply. For example, as the demand for COVID-19 vaccinations spiked, pharma companies were tasked with delivering the appropriate quantities of vaccines to numerous distribution sites. If too many vaccines were sent to one place, they would be wasted due to insufficient cold storage availability. Pharma manufacturers need real-time analytics to adapt to regional demand and prevent distribution bottlenecks and service disruptions while optimizing resource utilization.
Over the coming decade, resilient supply chain networks will be critical to navigating an increasingly turbulent market. Pharma companies that integrate flexibility and redundancy into the entire value chain and improve visibility will be best positioned to predict chain disruptions and respond rapidly and accurately.
Although most pharma companies and other CPGs have begun identifying strategies to leverage their data better, many are experiencing significant pain points trying to quantify the return on investment in data across business segments to inform investment decisions.
Per research from the Boston Consulting Group (BCG), roughly 90% of the CPG leaders cited data collection, activation and scaling as the primary obstacles to achieving their marketing goals. Advanced analytics and AI can potentially have a compelling impact on CPGs’ marketing use cases. In some categories, such as beauty, up to 45% of sales is attributable to marketing, per BCG’s research.
Even for other CPG categories, the impact of marketing on sales can be two to three times higher than in other industries. Therefore, the industry is uniquely positioned to capitalize on advanced analytics and conversational AI to make marketing more effective.
Closing Thoughts
CPGs’ and retailers’ success will depend heavily on their ability to respond quickly to trend changes. BI tools do an excellent job graphically summarizing large sets of structured and unstructured data. But BI platforms require data scientists and analysts to examine enormous data sets to get the real answers businesses need, which can take days or weeks.
NLG technology is the vehicle that not only gives CPGs and retailers a better understanding of their data but also makes actionable intelligence available quickly to a broader range of decision-makers, not just data scientists and analysts. This democratization enables companies to make faster, better-informed decisions and to know what’s happening, what may be coming and what to do next. It also represents an unexpected and refreshing breakthrough in technology.
Conversational AI – when it produces authentic, dynamic, multiturn conversations by pairing NLQ (natural language query) on the human side with NLG on the machine side – stands to ensure that humanity remains at the heart of artificial intelligence.
By giving machines the power of language, we’re also reclaiming some of our freedom from machines, giving knowledge workers more time to think and more to think about.
