We had another great webcast discussion on price optimization today, this time with Vendavo Business Consultant Dominic O’Regan and End-to-End Analytics Partner Gianpaolo Callioni. Not only did we identify some of the unique complexities around parts pricing, but we also uncovered how successful organizations are well on their way to the Holy Grail of pricing: Value-Based Optimization. Check out the webcast on-demand, and read our Q&A chat with fantastic questions from the attendees below.
COST + MODEL
Q – Wait – I thought that cost-plus is lazy and bad – you’re saying it’s good?
A: Pricing based on costs is often a great place to start, because most of the time, you need to understand margins as prices are set, as a guardrail even if you plan to move from inward looking data to outward focused information such as competitive prices, or attributes valued by customers, as you set prices.
Q: What about distribution/reseller channel where the exact same product is being competed by multiple companies? Does that make cost plus and competitive the most relevant?
A: Those are certainly great “guiderails” – making sure you aren’t selling below cost, or selling greatly below relevant competitive pricing but, data and analytics can take it even further…. There are some markets – especially in the EU – where you have to have strong reasons for different prices. But where you have sufficient data that could have attributes that drive willingness to pay, analytics will show great value in differentiated prices.
Q: We are testing and learning to identify how we can define price sensitivity within our product set, but the data is monstrous.
MDL A: Yes, the data is huge, and the attributes are not always apparent at first glance – that’s where AI and algorithms come in. Vendavo has taken project-based intelligence and converted that knowledge into software solutions.
GP A: “monstrous data size” is a blessing not a curse in this case!
Q: “So, Value Based pricing can use simple value rules to modify prices. It can also use derived attributes that reflect value, but both are based on manual selection of the rules or attributes?”
GP A: Yes, you’ll use analytical tools to manually identify the rules and attributes that are related to value, along with your business knowledge. However, the third method of Value-driven pricing – Optimized Pricing – uses AI to do the math and test out all the characteristics of your transaction data. You’ll clearly understand what factors are contributing to willingness-to-pay, and in the best systems, you’ll be able to adjust and augment what the AI solutions recommends, using your knowledge of the business.
Q: What is a proven metric to measure the success of specific price points? For context, parts that can change pricing every day. Is this something you measure week by week, month by month or year over year? Or none of the above?
GP A: You can measure your price lift and volume changes over any period in a systematic way using the causality analysis. It will show the impact of your price moves. If the data is stretched enough and the variation is significant, you may be able to even approximate the elasticity of the part, something very difficult to do in spares.
Q: What if some attributes are more important / critical to one segment? How can we be more dynamic in identifying the right value attribute for that customer/product segment?
MDL A: Dynamic really means you have the ability to respond with agility – either it’s very simple / small data set so you set up a manual statistical review, or you scale large data sets with AI / algorithms to do this work automatically, to signal changes in significance – for review and inclusion in the pricing tactical deployment.
PRICE TARGET SETTING
Q: GP mentions percentage-based pricing here – does that mean I’d used the same percentage – say, the 75% percentile – as my target across all my segments that are based on similar willingness-to-pay?
Dominic A: If your segments are doing a good job of grouping product/customer combinations by willingness-to-pay, applying a standard target (as a percentage, so it can be applied across all your segments) is a great place to start. With Vendavo’s “Power and Risk” approach, you can automatically assess the distribution within the segment, and have segment specific guidelines, to clean up long tails, or identify un-used pricing power.
MDL notes: A couple of folks noted that they weren’t quite sure that they heard the benefit correctly, so restating it here: yes, in one case, a Vendavo customer was able to find enough value in the first four hours of user acceptance testing (UAT) to pay for the entire project!
Q: what analytic tools does Vendavo give us to assist the pricing and segmentation – can the users decide the price based on the tool recommendation or we have to engage a specialist?
MDL A: Vendavo uses B2B specific techniques that we have developed over the years by working on projects with our customers. (specifically: Segmentation Manager, and Price Optimization Manager) We’ve made that expertise part of the solution – so the software – driven by AI/machine learning/algorithms offers up recommendations that the pricing role can review and revise as appropriate – a specialist or a scientist is not required because unlike black-box applets, Vendavo’s solutions provides context for the recommended price targets. Anywhere you feel you need to have a different basis for guidance, you are able to deploy that guidance to those who need it – at as high or as granular level as required.
MDL: Thanks for joining us today – looking forward to having you join us on the next Vendavo webcast, and have a profitable day!