How Advanced Analytics Is Changing B2B Buyer Expectations

by   |   March 24, 2016 2:00 pm   |   0 Comments

Javier Aldrete, Senior Director of Product Management, Zilliant

Javier Aldrete, Senior Director of Product Management, Zilliant

The rapid emergence of new digital technologies continues to present challenges for all companies – including those in the B2B space – trying to meet complex buyer expectations. Companies are expected to move at a faster pace than ever before and provide relevant information to buyers through an ever-changing variety of media.

For example, detailed data proliferated across the Internet continue to both dictate buyer expectations and provide convenient answers to customer questions. As new technologies are developed and new avenues to data emerge, speed and accessibility to information will be critical for companies to succeed.

As information continues to fuel and be fueled by new online channels, we most often hear about the impact this has on the B2C sales world. But as anyone in the B2B space will tell you, this evolution is far reaching and certainly relevant. Similar to B2C buyers, B2B buyers feel empowered by their access to data.

As a result of the rise of e-commerce in B2B and the general availability of data on the Internet, B2B pricing and product information is significantly easier to find and compare than before. This is enabling buyers to be armed with more information going into a price negotiation than was previously possible. This also means that buyers now expect companies to have relevant and convenient product and pricing information on their websites.

It’s All About the Customer Experience

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These expectations extend beyond product needs and pricing information. They also affect how buyers want to engage with a company – online, on the phone, or in-person – and, often, the customization across each channel. Buyers want self-service when they want it and they want to speak with a sales rep whenever they want, creating the need for companies to deliver consistent, easy, and relevant experiences across channels. In addition, sales reps are expected to be significantly more in tune with individual customer needs, including having relevant pricing and product recommendations on-hand at all times. Fueling these needs further is the fact that customer preferences are colored by their personal purchasing experiences, putting standards at an all-time high.

To meet these expectations, B2B companies are leveraging advanced analytics. Analytics can help companies to customize buying experiences through ecommerce channels and to provide sales reps with guidance about what customers are likely to purchase and what prices make sense to quote in the context of the deal. Customers want their buying experience to be finely tuned to their unique needs from a product perspective as well as from a pricing perspective. Across channels, whether on the web or in sales rep interactions, B2B customers expect companies to be able to anticipate their needs.

With all of these perceived demands in place, the learning curve for sales reps can be large and overwhelming. B2B sales reps are often tasked with managing between 50 and 100 accounts and selling a broad portfolio that can include hundreds of thousands of products, making data and analytics the only means by which to manage decisions, such as the following:

    • Where is my time best spent?

 

    • Which products should I talk to this customer about?

 

    • What prices should I quote to win the deal but do so profitably?

 

Because salespeople are now supposed to know these answers in advance, descriptive analytics and manual reporting have been rendered largely useless for sales reps.

For B2B companies with repeat customers, transaction data holds a wealth of information about where and how to sell more for more, but much of this information goes undetected. Algorithms and predictive models are needed to do the heavy lifting to turn this data into actionable information about each customer for sales reps. This information, or prescriptive guidance, enables sales reps to have more strategic conversations with customers and, in turn, builds the sales team’s confidence in advanced analytics. This same actionable information also can easily be fed into ecommerce platforms to provide a personalized web buying experience for customers.

Timing Is Everything: Shifting Workforce, Shifting Analytics

The shift from traditional to advanced analytics comes at a unique time, when the more experienced sales reps in industrial B2B companies are retiring and a new wave of millennials are entering the workforce. In these companies, where product and customer proliferation is massive, the learning curve to meet buyers’ consultative expectations is huge. The challenge lies in infusing a new rep with the knowledge of a retiring rep – which can include decades of customer and product knowledge. As a result, companies will be looking to advanced analytics to serve as a guide for new sales reps, effectively shortening the learning curve and helping them to meet customer expectations more quickly.

As the era of big data continues to evolve – making even more information readily available and increasing the volume and complexity of customer demands – it creates overwhelming pressure on B2B companies to solve problems, deliver a consistent omnichannel experience, and effectively equip sales teams for success. But with the right tools in place able to analyze the available big data, companies should be able to evolve with the times accordingly and continue to provide superior service to their customers.

Javier has more than 16 years of experience applying advanced business intelligence and predictive analytics to solve business problems in multiple industries. As Vice President of Product Management, Javier is responsible for Zilliant’s Sales Optimization product offerings’ vision and roadmap. In this role, Javier works closely with many customers helping maximize the value they get from Zilliant’s SaaS solutions and bringing their real-life business scenarios into new R&D efforts.

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