How can machine learning improve your B2B online sales?

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B2C eCommerce saw amazing growth and global adoption over the last decade. However, global B2B eCommerce market is a much larger pie, several times the size of the retail one.

With businesses struggling to reach, attract, and retain customers, digital marketing expertise is becoming one of the most sought-after skills with many eCommerce merchants.

And with the latest technology trends, there is significant push towards marketing and sales automation. How can B2B businesses leverage machine learning to improve their results? We have some of the answers right here.

At B2B Marketing Expo in London we will present our digital marketing services and results we have achieved for our clients.

Our Digital Marketing Team Leader and eCommerce Consultant Vanja Bunjevac will give a presentation entitled: Data-driven decision making: Embracing technology for triple-digit growth. I spoke with Vanja to help him prepare for the presentation, but also to share some of his thoughts and expertise with our readers.

Vanja, you have worked with a number of B2C and B2B clients over the past several years. Your work revolved mostly around planning and executing digital marketing strategies that led to some amazing results. Can you pick one project you are most proud of and why?

Currently, I am proudest of the work we have done on the KEH Camera project. KEH Camera is one of the largest second hand US camera stores, buying and selling camera equipment, where we were quick to embrace 3 new technologies on the market to get ahead of the competition.

Behavioral audiences, data-driven attribution modeling, and programmatic ad delivery. This was the first time we worked on such a big project and the results ended up to be above all of our or client’s expectations.

What is the (secret) recipe for achieving triple-digit growth? Can it be achieved in all industries?

The secret recipe is to focus on user behavior rather than standard audience segmentation. Examples of this is search intent and how the user used and navigated the site over time. With audiences built using this behavioral structure, plus programmatic ad delivery and data-driven attribution modeling, the effectiveness of the campaigns and return on ad spend (ROAS) just skyrockets.

Can it be achieved in all industries? I don’t know. But so far, clients in all the industries we worked were pretty successful.

Now, let’s try to share some real-life examples (some of those you will present in London, but let’s give our readers a sneak peek into your presentation) – how does machine learning help you as a consultant and your clients make good decisions?

Machine learning algorithms play a major role in today’s digital marketing. We use it in two ways in our strategy buildout. One is in understanding the multi/omnichannel attribution, to identify which ads in users journey contributed the most towards the goal – conversion. And the second one – automatic bidding for the stores where the amount of products is too large for a human to effectively optimize for the best results.

I know you and your team often come into a project and realize that, in order to fully utilize some of the online marketing possibilities, you first need to get rid of the technical obstacles. Can you share what are some of the common challenges when trying to optimize the feeds and campaigns for your clients? And how do you go about them?

There can be many obstacles for an effective digital marketing strategy. First is definitely the usability of the site. If the site is not easy to use nothing can help you out in growing your online store. For these kinds of issues, we have our designers and frontend developers who can resolve all such issues and remove any obstacles between your users and conversions.

The second issue we stumble upon quite a lot is the low-quality product data and taxonomy. Usually, the store categorization is built by professionals who understand the products they are selling which can be an issue for new users. What we do is an extensive keywords research for each major category, trying to understand how users understand the products you are selling and build the categorization based on the insight we get from the research.

That way we enable stores to grow organically and everything is well prepared for paid marketing activities.

How to find us at B2B Marketing Expo?

Vanja will take the B2B Marketing Expo stage at ExCel London on Thursday, March 28th. The presentation starts at 10:15 in Theatre 25, so make sure to mark your calendars. And if you are interested in learning even more about how we can help you improve your online sales firsthand, meet us at the event.

You can:

  • schedule a meeting with us before the conference HERE
  • come join us at our stand – the number is #1670

I’m not attending the event, but would like to learn more!

Great – if you’re not coming to London, we can still help! Just leave us a note via our contact form and we will get back to you to get the conversation started.

Some of the topics we’ll want to cover with you, so you can think about them ahead, are:

  • current state of your business and where you want to be
  • your target markets and customer profiles
  • your current digital marketing efforts (what channels are in use, and how)
  • any issues/challenges that are hindering growth from your perspective

Let’s see together how we can help you get that triple-digit growth for your business!

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3 comments

  1. I personally feel that the B2B online sales are largely dependent upon digital marketing at least for now and it is, therefore, to first channelize our efforts in digital marketing and slowly and gradually adopt the changes in it.

  2. Machine learning algorithms are the essence of Digital marketing. Soon, we will see all the companies incorporating their use in the development of the best online marketing strategy.

  3. Thanks, Aron and Vanja for crucial information. Machine learning is advanced technology, and so not all companies are capable of empowering applications with machine learning. I guess, it needs few more years for the development companies to enable the usage of machine learning.

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