So, you’ve begun to make use of big data and advanced analytics in order to drive greater value from the insights derived from your marketing and sales efforts. You may not have realized it yet, but your next step should be optimization – utilizing data to manage prices throughout the selling network more efficiently than ever before.
This is easier said than done, however. After all, while you’re busy crunching numbers and analyzing customer data to find new ways of targeting the best potential markets and maximizing their value, your competitors are just as likely to be doing the same thing.
The bottom line: You need a way to not only get ahead of your competition, but stay there. To learn how you can optimize prices throughout your selling network, check out the following list of five key areas where big data and analytics are already having a positive impact.
1. Pricing on a Global Scale
One of global trade’s greatest challenges is that individual markets often go their own way when it comes to pricing. This can be especially problematic for companies who do business in more than one country, as they may have to contend with several different sets of local prices at any given time – which means lower profit margins if they charge anything other than the lowest price possible in every region of operation.
This happens not just because there are differences between countries’ economic conditions and buying power, but also because of differing tax regimes, distribution and customer needs. In order to find the optimal price for an individual market, you need to be certain that prices are set based on what potential customers can afford in the country where they live.
This is something that organizations like Cargill have been grappling with recently. The global food ingredients supplier has yet to perfect its method of pricing products across borders – though it’s relying on big data to help get there soon by leveraging insights from information gathered about suppliers and its retail partners themselves.
2. Effectively Adding Value While Preserving Margins
Every company should know how much money it needs to make in order to stay afloat. That should go without saying; but because companies also need room for growth, their sales teams often prefer not to raise prices too much if they don’t need to.
This is why many companies struggle with the concept of pricing, because they aren’t confident in what is a reasonable increase and how much profit is enough. But if you don’t price your products so that they generate adequate revenue for your business, you could be leaving a lot of money on the table – which isn’t something anyone wants to do these days!
However, there is a way around this issue: by finding ways to add value to your products without raising prices too high. This means keeping up with market trends and making sure that changes in customer needs are taken into account when setting prices. If you can provide more value at comparable or lower costs, then it’s possible to avoid having to raise prices significantly while still realizing greater profits.
One of the most successful companies in this regard is Amazon. Its prices fluctuate based on what it thinks customers are willing to pay, so they may go down or up at any given time – but there’s no doubt that its success is in large part due to the fact that it knows how to add value while preserving margins.
4. Gaining Retroactive Price Transparency for One-off Transactions
Retailers who work with wholesalers or distributors in order to buy products and sell them in their stores often don’t know when they’re getting a good deal on inventory until after the fact, when they can compare what was paid versus what price could’ve been set for sales purposes However, if you make use of these resources to drive revenue, you should get the best price possible. Big data is already being used to make this a reality by providing retailers with greater insights regarding what they pay for their products, which lets them set prices that are more in line with what customers are willing to pay.
For example , one European retailer began using advanced analytics capabilities to reach out to suppliers whenever its margins decreased. It then worked with them directly to find ways of increasing margins while continuing to provide value – thus ensuring that it priced items consistently across its stores worldwide, but also added additional value at every level of the supply chain.
5. Making Pricing More Agile While Maintaining Revenue
Not being able to change your prices quickly enough can be just as bad as charging too much. It’s hard to get customers excited about your new products and services if you can’t provide them with discounts for trying something new, or if they fail to respond to the value that’s being provided.
This is especially true in the B2B world, where companies often struggle when it comes to making pricing changes in a timely fashion – something they fear will cost them revenue in the long run. But in many cases, they’re overcomplicating matters by not taking into account how their own organizations work or what types of solutions might be available from others in similar situations.
For example , there are few things more frustrating for an IT professional than when a business partner doesn’t have complete knowledge of its software licensing agreements. In many cases, the only way to find out who’s responsible for a given problem is by going through a long and expensive process of logging a support ticket.
But when software companies use big data to provide better insight into their product portfolios , it becomes much easier for solution providers to recommend something that meets end-users’ needs without having to worry about how it fits with existing contracts. Instead of forcing partners to waste time on back-and-forth negotiations, they focus more on providing customers with solutions that meet immediate needs – which then helps them build stronger relationships based around recurring revenue streams that can be optimized over time.
6. Delivering More Value at Competitive Prices While Maximizing Revenue
Not everyone likes low prices; some people prefer quality over quantity when making purchases. Others may be willing to pay more in order to get the same level of quality, but still believe that companies should always strive for a competitive price point. However, going above and beyond is often necessary if you want customers to remain loyal – which is why it’s important that big data makes it possible to provide both value and savings simultaneously.
For example , a major department store chain has been using advanced analytics capabilities in order to gain a better understanding of how much its products are worth on the secondary market. It can then use this information to determine whether or not its selling prices will encourage long-term customer loyalty while potentially increasing revenue at the same time by selling higher-quality merchandise at lower prices.
7. Making More Timely and Accurate Decisions
In many cases, companies find that they get bogged down with too much data that’s not being used in a meaningful way. In other instances, the data that is being analyzed isn’t even real-time – which means it can’t be used to make timely adjustments when needed.
This often happens in retail , where the analysis of enormous amounts of data (including sales transactions and customer behavior) takes too long in order for managers to make intelligent decisions in real-time. But by using advanced analytics software to access this same information in a matter of minutes or even seconds , companies gain a number of benefits:
For example , one major retailer was able find the right price point (and the right pattern of mark-downs) by combining historical data with real-time information about weather conditions, which allowed it to avoid overproducing items that weren’t likely to sell.
8. S elling Higher Quality Products at a Lower Cost
In many cases, companies find themselves stuck in a vicious cycle where they’re unable to increase prices even if their costs go up – because customers know that they can simply buy from someone else instead. In other instances, price increases might meet some resistance as well as making it harder for new customers to try out a given product or service – which means you’ll have lower long-term growth rates as well as potential customer defection issues.
In most cases , this competitive pricing pressure is caused by online retailers who are able to offer lower prices because they don’t have the same overhead costs as traditional brick-and-mortar operations – which is why it’s so important that big data analytics tools give companies better insight into all of their costs. By using advanced algorithms to access this information, managers gain a more accurate idea of where his or her business is making money and where it’s losing out – which helps them understand what kind of pricing model needs to be used in order to cover costs while still maintaining customer loyalty.
9. Enhancing Marketing Campaign Performance
Big data has already revolutionized the way many different types of organizations approach marketing , including telecommunication providers, mobile carriers and even government agencies. But one area where it’ll make an even greater impact is in helping with lead generation and nurturing. For example, managers will be able to predict which customers are most likely to respond to a given offer based on their purchase history, demographics or even their social media activity – which allows them to target the right message at the right time in order to improve response rates and decrease customer acquisition costs.
10. Accelerating Cycle Time
In many cases , a company’s products or services may not need any adjusting when it comes to pricing because they’re already competitive enough in the marketplace. Instead of price increases, these products might benefit from more targeted offers that would help increase sales volume over time – which means you’ll have higher long-te rm revenue growth rates as well.
By using big data to identify these types of opportunities, companies can make the necessary adjustments without incurring any unnecessary expenses. In fact, in many cases they can create a more responsive sales and marketing model that will help achieve higher levels of success thanks to real-time information.
11. Optimizing Cross-Selling Opportunities
In many cases , managers have a much easier time optimizing prices than they do when it comes to identifying cross-selling opportunities . In fact, one study found that almost 70% of organizations were dissatisfied with their ability to execute personalized cross-selling strategies – which may be due in part to the way customers perceive this type of activity from larger corporations .
One common example is in e-commerce settings, where customers may find it off-putting if they notice that a company is suggesting multiple products that they could buy at the same time. However, by using big data as well as real-time information about the customer’s location and activity (which can come from their mobile devices), marketers can deliver personalized offers without making them seem intrusive or impersonal.
12. Increasing the Ability to Segment Customers
In many cases , managers are forced to use easy-to-identify demographic and geographical characteristics in order to identify and target specific groups of people – even though these criteria aren’t always very effective for targeting individuals with unique interests and behaviors . By leveraging various forms of unstructured data, however , companies can gain a much better understanding of their customers.
As a result, managers are able to use this information in order to reach larger groups of people who share similar interests and characteristics – which may be extremely important when it comes to reaching niche audiences with strong buying power . This ability is also expected to help increase the probability that they’ll see ROI based on their marketing and sales strategies, leading to higher return-on-investment levels for organizations as well.