Inbound Sales and Next Best Offers

The inbound traffic to the Contact Centers is getting increasingly important as a sales channel. Many financial institutions have noticed that the outbound channels are losing traction. Especially the human touch points where customer facing personnel can talk with the customer and identify latent financial needs. Few retail banking customers want to visit the branch any longer. True, the pervasive use of cell phones and mobile banking solutions is an important explanation to the drop in branch visits but it is not the only one. The banks themselves are partly to be blamed for this changed customer behavior with an aggressive segmentation of the customer base and reduced cash handling in the branch network. The insurance companies experience similar drops in the effectiveness of their outbound sales as prospects stop using plain old telephones and opt-out from sales calls. There is an old rule of thumb in sales that it is seven times costlier to sell to a prospect than to an existing customer. I do not have the numbers but I assume that the same factor applies to the sales effectiveness on outbound versus inbound calls. I will argue in this blog post that few financial institutions reap their full potential on inbound sales and that most actually do their agents a disservice when they provide them with a complete Customer 3600 view as the only tool for increased cross sales.

In many cases, the inbound call from the customer will probably be related to an issue that is unrelated to the customer’s latent needs. So, a successful agent will need to resolve the initial issue and then quickly find the next best offer so that the client dialogue can be transformed into a need analysis on a possible latent need. The shift in mindset from problem resolution to need analysis may be stressful to the agent. Personally, I think that few agents will try to make the jump unless they get the support to identify the next best offer. And asking the agent to look at the listing of all the products that the customer has to identify the missing ones will not work. In my opinion, you have to help the agents see the forest for the trees if you have an ambition to increase cross-sales on inbound calls.

There are a number of different approaches to generating next best offers. Most of them use advanced analytics algorithms similar to the ones that Amazon use when they advise on what book to purchase next. But it doesn’t have to be that complex. I use the following model myself to classify different next best offer (NBO) approaches:

  • What do we want the customer to buy (campaign based NBOs)?

The first category is the easiest to implement but the least effective. You push the same offers to all retail customers, probably in the same manner as you do on your public pages. The only logic that is applied is that you exclude offers linked to products that the customer already has. This approach may be the first step in providing next best offers to agents and is probably better than not providing any Next Best Offer capabilities at all, especially if the offers are presented consistently through the channels with well-orchestrated campaigns. Hence, I call offers in this category for campaign based offers.

  • What do we think the customer ought to buy (rule based NBOs)?

The next category is slightly more advanced and customer centric than the previous one. In this category, you apply rules and rules of thumb to the available information you have on your customers to generate offers that ought to be relevant to the customer. The complexity of these rules may differ. A simple rule may be that you inform the customer on products that will expire soon. Information on a bank card that will expire may be the opportunity to up-sell a credit card. Similarly, you can suggest that the customer should roll the warrant that will expire soon into a new one with the same or similar underlying assets and risk exposure. It may be tempting to use the information on risk reward preferences that customers enter in saving guides on the open pages to generate rules based next best offers but I think that most will hesitate considering the recent MiFID II directive and ESMA guidelines.

But I expect that we will see an increased use of personas when generating NBOs in the future, especially from the banks that have segmented their customers based on their wealth. They will realize that they cannot neglect the “long tail” that constitutes the bulk of their retail customers, especially if they can minimize cost of sales by leveraging the in-bound contact center channel. The banks will use available information to group customers into personas based on their phase in life. And for each persona, they will set up a model portfolio with products an ideal student, family or senior citizen should have. Similarly, the same logic can be applied to Small and Medium Business (SMB) by grouping the customers based on their industry segment. I call this approach for rule based offers.

  • What has the customer indicated that he wants to buy (intent based NBOs)?

Few banks delivers on the omni-channel promise today. They seldom capture and act on the customer behavior on the digital channels. Users of the third category of NBO engines realize that customers will use different channels throughout their buying process. They try to intercept the customer intent as indicated by visits to product pages on the open pages, topics on the web chat and questions on the banks Facebook page. This category differs from the previous ones in two aspects. Firstly, the fact that is based on customer behavior and not suggestions originated by the bank is appreciated by execution only players that fear the legislations in giving advice. Secondly, information on customer behavior is more urgent than the other categories that try to expose latent needs. Many insurance companies realize that time is of essence so they feed their outbound sales team with information on prospects that have asked for a quote on a car insurance. The same engine may service the agents working with inbound calls assuming that only recent offers are presented. I call offers in this category for intent based to stress the urgency and to differentiate them from the others that try to expose latent needs.

  • What have others like the customer bought (predictive NBOs)?

The fourth and last category uses advanced predictive analytics to group customers based on their transactions. By grouping the customers with a similar “portfolio” of products, they can make predictions on what is the expected value of each possible next action for a customer by looking at the actions that peers with the same “portfolio” have taken. Contrary to the rules based model, the predictive analytics approach will be based on the “customer path” that other customers have chosen and not necessarily the one that the bank recommends. In a previous blog post, I presented some metrics from 15 banks that increased their cross-selling with 50-90% using predictive analytics so I think it is fair to assume that we will see an increased interest for this category of NBO in the future. Given the fact that I have referred to all the categories as Next Best Offers, it seems inappropriate to name this category Next Best Action as most people (myself included) do. Going forward, I will refer to this approach as predictive NBOs (or alternatively predictive NBA).

To summarize, the inbound channel will become an important sales channel where it is possible to increase cross-sales with 50-90%. But, in order to reap the cross-sales potential, you have to help the contact center agents see the forest for the trees by providing next best offers. A detailed 3600 customer view on all the customer’s products will help the agent provide excellent customer service but you need suggestions on next best offers to cross the chasm from problem resolution to need analysis during the dialogue with the customer.

Categories English, FeaturedTags , ,

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this:
search previous next tag category expand menu location phone mail time cart zoom edit close