The relevance of AI-driven product innovations in the current fast and data-driven market is beyond dispute. Artificial intelligence acts as an agent of innovation and empowers companies to outclass their competitors, enhance customer experience, and bring efficiency to operations.
It is obvious now that AI-driven product innovations will change how businesses work in B2B e-commerce. For instance, the ability of AI to analyse large amounts of data enables the prediction of market demand and orientation to product strategy.
That would form the foundation for actionable insights from customer feedback and market research through natural language processing and machine learning.
AI-based tools deliver better knowledge of changes in user pain points and preferences.
Furthermore, it allows the possibility of experimentation and optimisation due to concept testing and automation of A/B testing.
Indeed, Artificial Intelligence is transforming product development and becoming a driver of innovation in companies across industries.
Since it holds huge potential for improving strategic planning and execution, such expectations must be carefully managed in respect of the current potential of AI.
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Strategic alignment of AI initiatives with product roadmaps
AI initiatives offer valuable insights into all stages of the product life cycle, from process automation to data analyses and decision-making.
Groupon has been quite successful in implementing AI in enhancing operations and customer experience. For instance, the implementation of AI-powered quality monitoring tools improved CSAT and MSAT.
In particular, this strategic improvement has ensured service quality to provide services to customers and merchants during the pandemic.
So it is fair to say in the beginning that companies that adopt an AI-driven strategy are on firmer ground since the AI integration into product roadmaps is a competitive advantage for a company today.
Practical use cases of AI in enhancing e-commerce operations
AI technologies such as machine learning and natural language processing are definitely going to make roadmap generation easier.
ML algorithms analyse historical data for patterns to forecast demand accurately, in an effort to optimise inventory levels and discover sales opportunities, allowing a company to avoid stockouts.
Taking it another step further in terms of personalisation, the ML-based recommendation engines will promote relevant products based on customer browsing and purchase history to maximise cross-sell and upsell.
In addition, ML models can also lead to real-time adjustments in price, taking into consideration competitor pricing, demand patterns, and customer segmentation to increase profitability.
NL-based chatbots and virtual assistants provide customer support in real time and order processing 24/7. Among other things, applying the techniques of NLP to customer feedback analysis may help discover pain points and measure satisfaction to improve products.
Computer vision is a fast-growing area of AI that is also changing B2B e-commerce in particular ways. CV algorithms can identify products from images and facilitate visual search and augmented reality experiences that enhance the customer journey.
Additionally, CV tools detect defective products with ease and ensure the consistency of quality throughout the supply chain process.
Using pictures of merchandise, for instance, can identify relevant trends to project what will soon be a popular demand in the market.
One convincing example of AI’s impact in B2B e-commerce comes from my experience at Groupon. Guiding the sellers to price setting that will attract customers with AI-powered tools, Groupon has raised a 45% increase in conversion rates for some portion of their inventory.
The bottom line for this success is how Groupon managed to give insights into customer behaviour, preferences, and price sensitivity by using AI algorithms.
It also provided for a simplification of the user journey and more efficient capacity management through its AI-driven channel management solution.
In a nutshell, AI has played a huge part in changing Groupon into a company that is able to give highly personalised yet efficient service to both customers and sellers.
This goes on to show the potential of AI in enhancing decision-making and driving growth in a B2B context.
Leveraging AI to optimise supply and demand dynamics in B2B contexts
Companies using AI experience a 15% cost reduction in the supply chain and faster cash-to-cash cycles.
AI accomplishes this through demand forecasting and pattern searching in sales data. Armed with this information, a business is more likely to be prepared with the proper inventory levels aligned with forecasted demand, reducing carrying costs and bringing improvements to the efficiency of the entire supply chain.
Also AI may be used to smoothen the supply chain process through route optimisation and maintenance needs prediction.
AI-driven tools provide the whole tractability of the supply chain and assure on-time delivery for improved customer satisfaction.
While dynamic pricing in B2B contexts may be complex, AI could help analyse customer behaviour and recommend optimal pricing.
This will allow companies to react to changes in the market without losing out on competitiveness, since it’s also about not sacrificing profitability.
Techniques for automating large-scale merchant onboarding
Other strategies are effective to keep AI initiatives focused on broader business objectives including setting expectations for what is realistic and relating applications of AI back to company goals.
Groupon has managed to use AI in automating the onboard process for merchants. As a Lead Product Manager, I found a way to boost efficiency by Apify and OpenAI tools integration.
Apify’s web scraping pulls relevant information from several sources into one pipeline, making the gathering of information much easier. OpenAI language models enriched this data to build insights and automate communication.
As a result, Groupon onboarded over 10,000 merchants per quarter and guided them optimising deals based on supply and demand.
Effective management of AI implementation expectations
The limitations to AI implementation and potential risks associated with it include a lack of explainability and a need for human supervision.
The AI models need good, structured data, which requires investing in data governance. Demand forecasting, personalisation, or process automation are just some of the areas in which AI can drive maximum value.
In other words, AI is not a panacea for all challenges. While AI has a lot to offer, cautious optimism is vital.
Firstly, understand the specific needs of your business and then choose relevant AI solutions that would help in achieving your goals.
These can include personalisation and automation tools to increase operational effectiveness and customer experience described earlier.
Start pilot projects to understand the worth of AI and get feedback from customers. Secondly, monitor the performance of all your AI efforts, but also get insights from your team.
Engage experts from IT, marketing, and operations to define requirements and implement AI solutions.
And, most importantly, cultivate a culture of innovation. With strategic AI integration, businesses can enhance collaboration, improve decision-making, and create more agile product roadmaps, driving innovation and growth.
That is why it is crucial to manage expectations and align initiatives with business objectives. And it’s possible to do so with realistic goals and proper investment in the right tools and talent.
By: Vikram Haridas
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