YT YouTube2Text

YouTube2Text → Transcripts → Unlocking Profit with AI: Optimizing Contracts and Special Price Agreements

Unlocking Profit with AI: Optimizing Contracts and Special Price Agreements — Transcript

by SPARXiQ · 5,081 words · 819 segments · language en · Watch on YouTube

Open in transcriber Summarize with AI Download SRT/VTT

Full transcript

0:00good afternoon and welcome to the
0:02National Association of wholesaler
0:04Distributors webinar unlocking profit
0:07with AI optimizing contracts and special
0:10price agreements first I'd like to thank
0:13our sponsor today Sparks IQ Sparks IQ
0:16solves business challenges by enabling
0:19data-driven decisions and equipping
0:21Frontline sales teams with skills and
0:24accelerated results working closely
0:27together since
0:282014 Sparks IQ and naw partner to
0:32support its distribution members with
0:35Innovative and insightful Solutions and
0:37resources to accelerate their business
0:40performance I'm very excited to welcome
0:42our guest David Bowers president and CEO
0:45of Sparks
0:46I David has been committed to helping
0:49companies accelerate sales and
0:51profitability with the right analytics
0:54tools and complimentary skills training
0:57since he founded the company in 1993
1:01a couple of things to note before we get
1:03started if you'd like to ask a question
1:05please submit them via the Q&A function
1:08at the bottom of your screen this
1:10webinar will be recorded and posted at
1:13mew's digital Warehouse which is our
1:15online resource
1:17Library without further Ado David I'll
1:20turn it over to you what do you have for
1:22us today thanks we're here today to talk
1:24about uh how to optimize contracts and
1:27special price agreements and and uh this
1:30is a very timely presentation I hope as
1:33a lot of companies have significant
1:36amount of contract activity going into
1:38the fourth quarter with agreements some
1:41agreements uh expiring or being up for
1:44renewal at January 1 so this is
1:48hopefully a topical topic for
1:51everybody so in terms of today's agenda
1:53we'll be talking about the importance of
1:55contracts and special price agreements
1:57uh in terms of how they build account
2:00position how they acquire new business
2:02help acquire new business how to become
2:04a significant part of organic growth for
2:07Distributors we'll talk about a little
2:09bit about the challenges that they bring
2:12with them over time as they keep getting
2:15added to uh to the business and then
2:18we'll talk about some of the new
2:20technologies that can help Distributors
2:22to get a handle on their contracts and
2:26csps and really how Ai and machine
2:29learning can automate a lot of the
2:30drudgery and uh help companies clean up
2:34the accumulated uh Messes in many cases
2:38of Decades of salespeople adding in
2:41special agreements uh and contracts into
2:44their Erp systems and then we'll leave a
2:46little bit of time for
2:48Q&A so first we'll talk a little bit
2:51about the importance of contracts and
2:52we'll use these terms uh interchangeably
2:55today contracts and csp's customer
2:57specific pricing a lot of the agreement
2:59ments are not technically contracts but
3:02um they represent commitments to
3:04customers and so we'll use those those
3:07terms more or less interchangeably today
3:10because from a a data perspective and an
3:12analytical perspective they have a lot
3:14of common
3:16characteristics so first of all we know
3:18that contracts and csps are very
3:21important for distributors in fact as we
3:24look across a broad cross-section of
3:26verticals we see for really strong
3:29distributors contracts can represent 50%
3:32or more of their annual revenue
3:34depending on obviously the vertical
3:36Market that they're in and the uh the
3:39customer base that they're targeting and
3:42we know that based on buyer preferences
3:45and the competitive landscape the
3:47contracts are essential to being able to
3:50win the business and whether it's a an
3:53ongoing account relationship or key
3:55projects these all have very uh visible
3:58and price competitive environments and
4:02uh companies really have to sharpen
4:04their pencils and become much uh much
4:08more focused in how they approach these
4:12accounts so we know that there's a lot
4:14of benefits to contracts in
4:18CSP they're really about new customer
4:21acquisition if they have they're
4:23structured correctly they can improve
4:26loyalty of customers over time we also
4:29know that with the right structure they
4:32can not only acquire new business but
4:34also become a significant engine of
4:36growth and
4:37profitability as companies properly and
4:41effectively manage the structure of
4:43those
4:43contracts over time with the right
4:46pricing in place they can help Drive
4:48greater adoption and lead to higher
4:50Market Basket diversity in terms of how
4:53customers buy from those
4:55agreements and because they're
4:57structured and focused on specific large
5:01account typically larger account uh
5:04opportunities they can often help lead
5:07to vendor cost support in the form of
5:09rebates or special pricing from those
5:13vendors which help us to increase the
5:15profitability help to use vendor money
5:17to acquire new customers and to drive
5:20the long-term health of the
5:22business with contracts we also help to
5:25formalize how buyers buy from the
5:27distributor and this can help to
5:30increase stickiness of that customer
5:32relationship reduce churn and uh overall
5:36provide a stronger base of stable growth
5:39for
5:40companies they also can help because
5:43they anticipate price sensitivity of
5:45different customers they can help to
5:46lower manual overrides while
5:49increasing the process discipline that
5:52leads to
5:56profitability so the challenge of these
5:59types of agreements is comes from a few
6:03basic attributes that we'll talk about
6:05here today so part of it is how they fit
6:08into the margin mix of a company and
6:11part of it is about complexity so one of
6:14the things we know about contracts is
6:17that they are significantly more
6:20complicated in terms of the sales
6:22process and how we engage with customers
6:25we also know they go through multiple
6:27rounds of uh of competition to get the
6:31customers's business they often have to
6:34be negotiated on a prolonged basis with
6:36special terms and
6:38conditions also we know that the sheer
6:41complexity of these agreements can
6:43become overwhelming over time it's one
6:46thing to review a single contract on a
6:49timely basis but as we start to add
6:51dozens or hundreds or thousands of
6:54contracts each with dozens or hundreds
6:57of line items or more we end up with a
7:00very significant uh amount of complexity
7:03in terms of what has to be managed and
7:06maintained within the Erp system and we
7:09also know that many contracts have
7:11varying durations they may have varying
7:13expiration dates which just adds a whole
7:16another layer of uh complexity to the
7:20management and review
7:22process so the most important thing is
7:24that to understand here is that
7:27contracts and csps have a significantly
7:30lower margin performance compared to uh
7:34standard system pricing so typically
7:38companies may have you know 50% or more
7:41of their revenue in that particular
7:43class compared to their standard pricing
7:46or their even their overrides in manual
7:48pricing as we look at the margins we
7:51typically see over a thousand basis
7:52points lower performance on contracts
7:56and csps compared to standard price ing
8:00which is similar to what we see with
8:02manual override type
8:04pricing so over time the tricky part is
8:08the sheer amount of
8:09complexity that sits in contracts and
8:12csps the the amount of Revenue that's
8:14going through this particular uh pricing
8:17method and then also the significantly
8:20lower margins that they bring so this is
8:23where if if we aren't rigorous in how we
8:26manage csps we can end up with a
8:29significant profit Sy coal in the
8:34business so Sparks IQ has analyzed
8:37thousands of contracts and looked at
8:42what are the um what are the positions
8:46that salespeople make when they assign
8:48products on or off
8:50contract what we've seen is there's a
8:52extremely high level of variability and
8:54how effective sales people are at
8:57understanding what should be on or off
8:59cont contract and um the consequences of
9:02course are very high so if we look at
9:05you know if we look at a customer's
9:07total purchases from a distributor we'll
9:10see that um on average 37 36.8
9:1537% of the items that that customer Buys
9:20have been correctly positioned within
9:22the contract and that's important
9:25because that's what drives wallet share
9:27on the most visible and sensitive
9:29product
9:30you can see how much variability there
9:31is around that number though in the top
9:34quintile they have 51% of what's
9:36supposed to be on the contract on the
9:39contract whereas at the lowest quinti
9:41only 7% is there so this is represents a
9:46significant amount of leakage in terms
9:48of wallet share when salespeople don't
9:52accurately understand the customer's
9:55buying profile and position those
9:57products on contract
9:59you can see on the upper right side
10:02these are there's a significant amount
10:04that is um maintained off contract a
10:07smart salesperson working for a smart
10:10distributor knows that not everything
10:12the customer buy should be on contract
10:15depending on the nature of the
10:16competitive environment the nature of
10:19the the product set that's being sold
10:22it's important to be able to maintain
10:24off-contract spend what we call non-
10:26declared spend because it will typically
10:29carry a th000 basis points are more
10:32profitability so we can see that on
10:34average of a customer's purchases the
10:37sales person will generate 16% correctly
10:40off contract and that helps them to
10:44offset the lower margins of the items
10:46that are on
10:47contract if we look in the lower half of
10:50this chart however we see significant
10:53opportunities here and by the way in the
10:54upper right you can see again how much
10:56variability there is the best sales
10:58people are getting 40% correctly off
11:01contract the worst are only getting 5%
11:03correctly off contract which means
11:06there's a significant margin degradation
11:08for those salespeople who don't
11:10understand how to maintain non-contract
11:12non- declared spend we look on the lower
11:15left side um of the customer's spend
11:19that's on the contract we see that
11:21there's a significant amount that should
11:22have never have been on the contract you
11:25can see on average 30% of a customer
11:28spend should not have been on the
11:30contract for the average salesperson of
11:33course when they fail to get that off
11:35contract they're selling at a th basis
11:37points lower prices generally speaking
11:41and again you can see the the sheer
11:43variability around these numbers the
11:46best quintile of sales people having
11:48only 5% that was U mistakenly left on
11:53contracts compared to the the worst
11:55sales people at
11:5742.3% vast ly overburdening the
12:00agreement with low price combinations
12:02that customers don't react to in the
12:06lower right side you can see U there's a
12:08big cost when salespeople don't
12:10understand what are the products that
12:12should be on the contract and you can
12:14see of the customers total spend there
12:17was on average
12:1916% of customer spend that was
12:23predictably price sensitive and driving
12:25wallet share and adoption that the
12:28salesperson failed to appropriately get
12:30on contract that typically leads to
12:33lower share of wallet and makes it
12:36harder to um to drive adoption of those
12:39contracts leads to cherry-picking Etc
12:41and you can see again the sheer amount
12:44of variability among salespeople
12:45negotiating these contracts and special
12:48price agreements the best quintile only
12:51has 3% that they missed on the contract
12:53that should have been on there whereas
12:55the worst quintile of sales people is is
12:59missing over half of what they should
13:00have gotten in terms of the customer
13:04spend so the challeng is it's clear that
13:08you there's a lot of money in this um 10
13:11a thousand basis points in an industry
13:13that on the average makes 400 basis
13:15points is a huge amount of money and
13:18what we're talking about here is how do
13:20we start to crack through the
13:22complexity of contracts and uh as we
13:26look at Distributors and their contract
13:28challenges we see commonly thousands of
13:31these contracts that are sitting in the
13:33Erp system typically there's hundreds or
13:36thousands of products on each contract
13:38depending on the industry there may be
13:41extremely variable vendor cost supports
13:44which um need to be renegotiated
13:46typically as those agreements come up
13:48for Renewal there may be inconsistent
13:52price factoring some of them as net
13:54prices others as list less factors or
13:57Cost Plus factors things like that
14:00we also see that there are unpredictable
14:03or scattered U renewal dates so some of
14:07them may be on a calendar year some of
14:09them may be one-year agreements some of
14:11them may be longer terms and it's very
14:14easy for Sellers and for management
14:17teams to lose control of this uh part of
14:21their business just based on the sheer
14:23complexity from a timing perspective and
14:27from a a data intensity perspective Ive
14:30in many cases salespeople don't start
14:32looking at these agreements until a few
14:34weeks or even a few days in some cases
14:37before they're up for Renewal and
14:39because they haven't uh spent the right
14:42amount of time using the right tools or
14:43the right processes they're often
14:46failing to make important adjustments to
14:48those agreements to drive sales and
14:51profitability and furthermore most Erp
14:53systems are terrible at helping to
14:56manage this process they're not designed
14:59for analysis or review or decision
15:02making they're designed more for
15:04execution
15:08purposes and there's lots of bad habits
15:10that creep into contracts and special
15:13price agreements we'll just review a few
15:15of them here but uh they are significant
15:19we see a huge amount of peanut butter
15:21pricing where the same margins are
15:23applied across product lines that have
15:26radically different levels of price
15:27sensitivity uh radically different cost
15:30to serve radically different vendor cost
15:32supports and uh these peanut butter
15:35practices leave a lot of money on the
15:37table and in some cases leave share of
15:40wallet on the table if they haven't been
15:42uh aggressive
15:44enough and we see that while many of
15:47these contracts have very aggressive
15:50pricing which was appropriate to win the
15:52customer years later those prices
15:55haven't been touched and later in the
15:57life cycle when the value to the
15:59customer has been built up over time
16:01when switching costs have increased many
16:04companies have not remediated their
16:07aggressive pricing to uh reflect the
16:10maturity of that customer
16:12relationship we also see huge amount of
16:15defects in contracts around failing to
16:18secure proper vendor cost support
16:21typically about 30% of vendor cost
16:24supports are missing in these agreements
16:26a combination of missing customers
16:28missing products or under supported
16:31customer product
16:33combinations in addition there is a
16:36significant lack of linkage between
16:39share of wallet contract adoption or
16:41cost to
16:42serve so a huge amount of pricing
16:46leakage in these
16:47agreements and of course when the
16:50process of contract management is lacks
16:54these bad habits tend to be extended and
16:58rolled over over year after year after
17:00year and thus becoming a significant
17:03profit black hole for the the companies
17:06that have
17:07them there's lots of other leakage
17:09points uh cost increases that haven't
17:12been indexed properly within the
17:14contracts inactive or underperforming
17:17customers and SKS promises that are made
17:21by customers and accepted credulously by
17:25salespeople that don't bear out in terms
17:28of actual real world
17:30performance uh are failed companies
17:32failed to remediate those again leaving
17:35tons of money on the
17:37table lots of wasteful discounting over
17:40overaggressive
17:42application of of discounts across too
17:46many product categories inappropriate
17:48products within categories just a long
17:51laundry list of discounting uh defects
17:56you think about it from a Six Sigma
17:57perspective
17:59and of course missing vendor cost
18:00supports in many Industries contract
18:03business um is successful to the degree
18:07in which it can secure a distributor can
18:09secure best vendor cost supports from
18:12their
18:14suppliers talked a little bit about that
18:16whole customer life cycle issue and the
18:19importance of adapting customer pricing
18:23over time as life cycle positions change
18:27and uh many companies just have haven't
18:29haven't developed any metrics of
18:32customer life cycle position or
18:35considerations and lots of times these
18:37agreements get um accumulate lots of new
18:40baggage over time as salespeople keep
18:42adding new products to the agreements
18:44not pruning appropriately the products
18:47that shouldn't be on the agreement over
18:49time more and more revenues being sold
18:51at lower margins into mature customer
18:56relationships and this whole issue of
18:58rolling expiration dates salese not
19:02being able to keep track of when they
19:03need to review not having the tools the
19:06training the process the technology to
19:10effectively review those agreements
19:12trying to weed through them in Excel
19:16spreadsheets and other uh Clumsy tools
19:19like that leads to a significant amount
19:22of just rolling over of agreements
19:24without proper review
19:29so with the sheer complexity of these
19:31agreements in terms of timing in terms
19:33of number of products number of
19:35customers these can quickly grow into
19:39hundreds of thousands of line item
19:41records in the contracts or special
19:44price agreement databases of companies
19:47the complexity of all of this has often
19:49escaped the company's ability to manage
19:53and to optimize and to rationalize those
19:56agreements fortunately now through
19:59machine learning and artificial
20:01intelligence there are tools that can
20:03help to automate a lot of the tedious
20:06parts of contract review be able to
20:09apply uh best practices from successful
20:12sellers to help uh average sellers to be
20:15able to improve their approvals and
20:17review of these agreements the pruning
20:19of these agreements Etc and although
20:22it's necessary to keep humans in the
20:24loop with these types of tools it's
20:26extremely important for distributors to
20:29really revisit how they manage contracts
20:32and
20:33csps it is becoming increasingly common
20:36in competitive Industries for companies
20:38to use these types of tools and
20:40Distributors who don't use them will
20:42find themselves with lower sales lower
20:45margins or
20:46both so some of the the tools in from AI
20:50can really help sellers do some
20:53important uh housekeeping here in terms
20:56of how they manage their agreements
20:58quickly we can help remove inappropriate
21:01products help to remove customers or
21:04combinations of customers and products
21:06that don't belong on these agreements we
21:09can also help to identify where are the
21:12opportunities for wallet share
21:14improvements and to make sure that those
21:17products are added into the agreements
21:18even when the the customer's own
21:21sales uh data don't show that they're
21:23consuming those we can infer those from
21:27important an analy ICS around customer
21:30purchasing behaviors and
21:33profiles uh obviously being able to
21:36understand the impact of vendor cost
21:38increases and recommending price
21:41adjustments to sellers to be able to
21:45quickly and efficiently make needed
21:46price
21:48changes and then also identifying across
21:51hundreds of thousands of line items
21:53where are the price factoring mistakes
21:55that have been made where we have lost
21:58the indexing of price to cost throughout
22:01those
22:02agreements and identifying as I
22:05mentioned before where do we need to
22:07engage with our vendors to get better
22:10cost supports we know that just as
22:12there's huge amount of variability in
22:14sell-side pricing there's also a lot of
22:16variability in buy side cost supports
22:18from
22:19vendors when we analyze the vendors um
22:23approval processes based on requests and
22:27the approved uh cost supports were able
22:30to start recommending best cost support
22:32combinations to those
22:34Sellers and of course to really help
22:38build more profitable pricing by
22:40reducing the over discounting of low
22:43sensitivity spending it by the
22:48customer so we talked about the
22:50importance and of these csps but also
22:53the complexity of it when you've got so
22:56many vendors so many different customer
22:59skew combinations in the Erp system all
23:02of these floating expiration dates it
23:05becomes especially important to automate
23:08and to apply new tools to make this
23:11process more complete uh lower effort
23:15and also to improve the results of it
23:18and as I mentioned earlier this is
23:20really not about replacing sellers but
23:23in uh supporting Sellers and making
23:25better decisions around their
23:27Contracting
23:29uh
23:29process and we do believe this will be a
23:32significant competitive Advantage for
23:34companies where contracts are
23:39important so some of the most important
23:41innovations that are going on right now
23:43in the contract World involve uh
23:45customer position analysis when
23:48contracts are set up we always hope that
23:50things will go well that customers will
23:52will buy from them but we know from The
23:54Real World that frankly that's the
23:57exception rather than than the rule so
24:00it's really taken some some advancement
24:03in how we understand customer purchasing
24:05Behavior to be able to predict what is a
24:09customer's a distributor's position with
24:12a given customer some of the platforms
24:14that Sparks IQ uh processes we have
24:17buyer
24:19automation uh platforms that help buyers
24:22to buy from their Distributors we've
24:23been able to train models based on buyer
24:28uh transactions across all of their
24:30suppliers to understand what are the
24:32markers and
24:33indicators of a distributor's
24:35position and with these types of of
24:38tools we're able to help really assess
24:41what is the the contract customer
24:43engagement level what is their adoption
24:45rate and how do we start to uh adapt the
24:49life cycle pricing based on
24:52that one of the most important uh
24:54insights we've gained is that typically
24:56Distributors can improve their margins
24:59on their contract business by between
25:01200 and 400 basis points while also
25:04improving how buyer Centric they are
25:06with their
25:09customers so contract position analysis
25:12is extremely important and we've really
25:15got four types of customer buying
25:17behaviors that we see commonly in
25:19distribution obviously can vary based on
25:22the vertical or the sub verticals in
25:25different markets but there are four
25:26different types that Sparks IQ has
25:29identified there's the U primary
25:33supplier which is where a distributor
25:36has achieved both High share of wallet
25:39as well as high Market Basket diversity
25:42there's secondary suppliers that's in
25:44the upper right um on the upper left
25:46you'll see secondary suppliers this is
25:49where they're getting a a significant
25:51amount of diversity but they're not
25:54getting the appropriate share of wallet
25:57in many of the C categories that they're
25:59selling into and then we have tertiary
26:01suppliers which is in the lower left
26:04where they're low on both diversity as
26:06well as Sheriff wallet and then in the
26:09lower right we have a couple of
26:12specialist categories where they are
26:14getting significant share of wallet and
26:17a relatively narrow Market Basket one of
26:21the um most pivotal analytics in terms
26:25of contract analysis is being able to
26:27understand not only the position but
26:30also the engagement score so every
26:33customer's position can be scored in
26:35terms of how effectively or how
26:37significantly they're buying from a
26:39given distributor and we're able to
26:41assess the gaps based on their uh their
26:45less than optimal engagement
26:51scores one of the most important things
26:53we talked about was customer life cycle
26:56and we know over time that customers
26:58will change how their position uh looks
27:02from one period to another and U as we
27:05come through the renewal process we want
27:07to be able to identify which customers
27:10are changing positions and here you can
27:12see a sample analysis that Sparks IQ
27:16provides for some of its contract
27:18analysis where we show uh what's going
27:21on and how many primaries are turning
27:23into secondaries tertiaries Etc and we
27:26want to be able to adapt our pricing
27:28either to capture better pricing when we
27:31move up in terms of position or being
27:35able perhaps to use carefully targeted
27:37discounts when there's a
27:39deterioration in customer
27:44position and over time we can start to
27:47build dashboards for contracts that show
27:49how many of the contracts are performing
27:52at primary secondary tertiary specialist
27:55levels and as we start to understand
27:58that more dynamically we're able to
28:00update our pricing standards we're able
28:03to prune and optimize contract
28:09structures in many cases there's gaps
28:12not only in terms of uh engagement
28:15scores but also of course purely in
28:17pricing or or sales performance we also
28:21know that there can be very significant
28:23differences in net profitability across
28:25these agreements so as you get into the
28:27contract
28:28space aggressively you want to be
28:31managing and measuring uh net
28:34profitability not just gross margin
28:37profitability and these new tools are
28:39able to help you really uh rigorously
28:43understand the true position that you
28:45have in all of these
28:49Dimensions so there are a few basic
28:52types of uh of outputs that come from
28:55this type of uh work work one is really
28:59building the appropriate recommendations
29:02for different types of customers getting
29:04their pricing squared away another is
29:08helping to secure appropriate vendor
29:09cost supports and uh making sure that
29:13vendors are playing the appropriate role
29:15in in winning these uh these
29:21deals few keys to uh improving
29:26profitability obviously we need to set
29:28appropriate pricing targets for the next
29:30term that means doing careful price
29:32analysis and uh relating that to
29:35customer life cycle and engagement
29:37scores reviewing and
29:41rationalizing the pass through cost
29:43increases pruning wasteful additions to
29:46contracts we talked about how many uh
29:48items are on contract that shouldn't be
29:50on contract getting them off so that we
29:52can capture higher
29:54profitability converting fixed Nets to
29:57factored pricing is another uh key to
30:01driving improved
30:03profits and then understanding the
30:05cherry-picking behavior of customers
30:07just because you sign an agreement
30:08doesn't mean the customer is going to
30:10buy from that agreement being able to
30:11measure and infer and to uh recommend
30:16improvements to those agreements to
30:18remediate cherry picking is
30:20critical and then getting better cost
30:22supports from vendors on the buy
30:26side so Sparks IQ is now uh offering to
30:29naw Distributors who have an interest in
30:32exploring this further uh a workshop to
30:35help you understand the opportunity in
30:37your business with a data extract of
30:41your contracts anonymized we can help
30:43you to quantify what is the the
30:46opportunity share with you um some uh
30:50demos of how these tools can help you to
30:52Wrangle your contracts into more
30:55effective management and really help you
30:58rebuild
31:00profitability so if you have an interest
31:03in exploring this feel free to send uh
31:05an email to hello Sparks iq.com subject
31:09contracts and we'll be happy to help you
31:12get started with understanding and and
31:15managing and optimizing your
31:18contracts so with that we will turn it
31:20over to
31:25questions thank you so much for that
31:27informative presentation David um I know
31:29our viewers found it helpful um I do see
31:32a couple questions coming in from our
31:34audience so up first what are the most
31:38common challenges Distributors face when
31:41it comes to optimizing spas and how can
31:45AI overcome them well it's the sheer
31:48complexity as I mentioned earlier um
31:50when you've got hundreds of thousands
31:52thousands tens of thousands hundreds of
31:54thousands of uh line items in the RP
31:58system that are predictably lower
32:01profitability just being able to manage
32:03complexity is super important and you
32:06know most people can't really process
32:08that level of complexity certainly not
32:09in spreadsheets certainly not um just
32:12through reviewing PDFs and that sort of
32:15thing we really have to start to
32:16organize them into a common data set we
32:19have to build rule sets that will help
32:22to identify uh and prescribe the most
32:26important changes to those agreement
32:28ments and to manage the entire process
32:30from a workflow perspective so it's it's
32:33really about organizing the problem
32:36building appropriate rule sets and
32:38decision criteria and then helping the
32:41Frontline sellers who manage those
32:43relationships to quickly and efficiently
32:45Implement
32:47changes wonderful thank you um I do have
32:50another question here uh what kind of
32:53data is needed to effectively Implement
32:56AI for optimizing contracts and those
33:00Spas um and how do companies ensure its
33:03accuracy oh well data accuracy is also
33:07another area where Ai and machine
33:09learning can uh can help in terms of
33:12being able to recognize bad data outlier
33:15data that sort of thing um but broadly
33:18speaking the types of data that you need
33:20are your standard invoice extract who
33:24bought from you at what prices uh what
33:27quantities what cost levels from your
33:29suppliers it's that invoice type
33:31transactional data combined with the um
33:34the contract structures themselves a
33:36list of the items that are on them and
33:39um what the standard pricing or other
33:41terms associated with them would
33:44be great um and I do have one last
33:47question here that I see coming in um
33:50and it's how does AI handle Dynamic
33:53factors such as fluctuating material
33:55costs or supply chain just disruptions
33:58when optimizing these Spa yeah typically
34:01we do that by factoring the prices in
34:05the system so we don't want to think in
34:07terms of net prices all the time a lot
34:10of cases what's more interesting is how
34:12those prices relate either to list price
34:14or to cost basis because over time if we
34:19we're constantly re-entering them or re
34:22let's say instantiating them into the
34:24system as net prices it creates these
34:27leakage problems around the the the time
34:30between when the vendor cost goes up and
34:33when we're able to recapture them so
34:35factoring is the most important step and
34:38I already talked about the importance of
34:40factoring in terms of uh preventing that
34:43type of leakage but it's also important
34:45to create comparability across thin data
34:48portions of a transactional data set we
34:51know some products we'll have very very
34:53few customers buying them and U
34:56factoring allows us to aggregate data
34:59from thin data sections into richer data
35:03[Music]
35:04elements wonderful well that is the last
35:07question that I see uh that I have here
35:10from our audience um so do you have any
35:12final words of Wis no just want to thank
35:15NW for uh the opportunity today and U
35:19thanks to everyone who attended and
35:22hopefully we can help you get your
35:23contracts uh at least explore your
35:26contract opportunity
35:28that sounds great well uh we are going
35:30to wrap things up and give everybody a
35:32couple minutes back on their calendar
35:34David thank you so much again for taking
35:37time out of your business scheduled to
35:39present on this topic um we always love
35:41having you um join us and I'm sure that
35:44our audience uh found a lot to think
35:46about and a lot of takeaways from this
35:48presentation I'd like to give one more
35:51uh thank you to our sponsor Sparks IQ um
35:54and just a reminder that Na's mission is
35:57to trans form the wholesale distribution
35:59industry begins with you so please
36:02contact us to learn more about our
36:03services events educational resources
36:07Innovation and advocacy naw provides for
36:10our members and with that thank you
36:12again David and thank you all for
36:14participating today I hope you have a
36:16wonderful day thanks everyone

About this transcript

This page contains the full transcript of Unlocking Profit with AI: Optimizing Contracts and Special Price Agreements by SPARXiQ, generated from the public captions YouTube serves with the video. The transcript has 5,081 words across 819 segments, with the original timestamps preserved so you can click any line to jump to that moment in the embedded player.

What you can do with it

Use the transcript to take notes, quote the speaker, build a study guide, generate a summary with ChatGPT or Claude via the YouTube Summary tool, or export it as a timed subtitle file with YouTube to SRT. You can also re-open it in the transcriber to translate the transcript into 100+ languages.

Free YouTube transcript tool

YouTube2Text is a free YouTube transcript generator — no signup, no daily limit. Paste any YouTube link and get the full transcript instantly, with timestamps, click-to-jump, translation to 100+ languages, AI prompts for ChatGPT, Claude, and Gemini, and exports to TXT, SRT, VTT, or Markdown.