Data anlytics and AI pricing

Datamine projects are customised for every client. Let us show you the factors which affect the price for your data analytics project.

How much does data analytics cost?

 

Estimating the price of a data analytics project isn’t straightforward, with a wide range of factors influencing the final cost. The quality of your data, the size of your business, your internal capability and the level of analytical detail your business requires – all make a difference. Cost also varies based on the tools needed – Datamine offers over 150 separate data, analytical and artificial intelligence (AI) solutions, and a typical project involves the combination of multiple solutions.

On this page, we review our key solutions, consider benefits and complicating factors, and provide a ballpark price for each solution type.

Disclaimer: We have over 150 different solutions, and a typical project usually involves more than one solution, so there’s a good chance you’ll be curious about one that isn’t on this page.  We're happy to share pricing guidance, just fill out the form at the bottom of this page and one of our team will get in touch with the pricing information for your unique situation.

Here’s what you need to know about the benefits, complexities and estimated costs of each solution.

 

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Analytics roadmap costs

An analytics roadmap translates your data strategy into a concrete action plan, guiding you as you implement new data initiatives. It also acts as a framework to measure the value and limitations of potential data projects, helping you focus your time, investment and capacity in the right places. The goal is to maximise the value of your data strategy and give you a clear path forward.

Pricing factors for an analytics roadmap:

• What stage of the process your business has reached – for example, whether you have a data strategy or analytics solution in place
• Your business goals, challenges and opportunities
• How many interviews and/or meetings needed
• The level of detail you require in your roadmap

 

Average analytics roadmap cost: $45k 

 

Real-life analytics roadmap example: 

One of our clients, an ASX-listed company, was not getting the traction that they needed – they couldn’t work out how to turn insights into tangible value.

The client reached out to Datamine for help, and we ran a workshop with the leadership, IT and analytics teams to set objectives and lay out a project timeline. From there, we assessed the data, examined past projects and long-term value, and worked with the teams to create a data strategy and analytics roadmap tailored to the needs of the organisation. The client now uses this framework to deliver projects and ongoing services and assess and shape new data initiatives. The price of this work was approximately $90k.

 

Further reading:  Data Strategy vs. Analytics Roadmap: What's the difference?

 

 

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Data mining and knowledge discovery costs

Data mining and knowledge discovery are often the first phase of a more comprehensive analytics project. This stage involves deep data analysis to strengthen your understanding of your customer base and identify challenges affecting your business. Data mining can yield surprising insights and outcomes, challenging your assumptions about customer behaviour and trends.

Pricing factors for data mining and knowledge discovery:

 

• The size of your customer base
• The amount of data work required
• The questions you want to answer
• The amount of external data (for example, market share or trend analysis) needed

 

Average data mining and knowledge discovery cost::$140K

Real-life data mining and knowledge discovery example:

After running market research, an insurance client wanted Datamine to dig deeper into their data to help them understand the results, with a focus on customer demographics, product preferences and market share.

The first step was a workshop with members of the marketing and leadership teams. During this session, we worked through answers to initial questions and wrote up a statement of work. The teams raised more questions, which meant a more granular analysis was needed.

Next, we cleaned and consolidated data from three databases to create a cohesive single customer view, which the business now uses as a dashboard for quick, reliable insights for their marketing and sales teams. Finally, we wrote up an executive summary of the outcomes of the analytics process, giving the business an invaluable source of truth about customer behaviour and sales trends. The price of this work was approximately$115k.

 

Further reading: Striking gold: What can data mining really do for marketing?

 

 

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Marketing automation costs

Our marketing automation solution is designed to consolidate all your marketing and customer management technology. Whether we’re working with your current automation or supporting your migration to a new platform, we deliver an integrated system, a cohesive view of all marketing and customer activity and a toolkit that allows you to automate key marketing processes. The goal is to optimise your approach over time, which helps you save time, reach more customers, and boost profitability.

Pricing factors for marketing automation:

• Your data volume and the need for cleansing or consolidation
• The complexity of your automation goals
• The amount of logic in your current data and whether that needs to change
• How much internal training and support required
• Your current automation platform and other technology systems and data sources
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Average marketing automation cost: $35k per month 

 

Real-life marketing automation example:

A client in the tourism industry wanted to elevate their marketing approach by switching from web and email services to a complete automation platform. After evaluating several platforms and choosing the best option, they approached Datamine to help with implementation and integration.

We worked with the client to implement the new software and integrate it with the existing single customer view. Once the system was up and running, we put key automations in place and trained the team. We’re still working with the client on an ongoing basis, providing support and maintenance as needed.

 

Further reading: What your marketing automation provider failed to tell you about its platform

 

 

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Analytical application development cost

An analytical application, or app, is a tailored analytics tool designed to deliver insights in a digestible format. Often built in the final stages of an analytics project, this app collects data, runs an analysis, and presents your results on a custom dashboard – for example, marketing results for the previous period or demand forecasting guidance. The goal is to make analytics an accessible part of the workday instead of a one-off process or monthly reporting process.

Pricing factors for analytical application development:

• App design requirements – including UX styling and ease of use
• Data quality
• Input complexity
• The level of testing required
• How iterative it needs to be
• The importance of real-time updates

 

Average price for analytical application development: $400k+ 

 

Real-life analytical application development example:

One of our banking clients needed a real-time pricing tool for frontline staff. While their sales teams had basic, Excel-driven reporting tools, they had no way to calculate personalised prices for financial products on the fly.

Datamine built the brand a custom web app that uses customer information to calculate a custom price and draw up a fee proposal in just a few seconds. For staff, it’s a valuable tool for customer engagement. For the business, it helps maximise the value of each sale. The cost for the algorithm and app development was approximately $245k. There is an ongoing operational and maintenance programme in place to ensure that the app is available and maintained as a critical front-line tool.

 

Further reading: Need a unique business application?  Bespoke > Enterprise

 

 

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Datamart creation costs

A datamart helps you leverage your data sources to maximise their potential value. Unlike data warehouses, which are a general repository for business information, this solution is usually built for a specific department or purpose.

A datamart consolidates data from separate databases to create a fuller picture – whether you’re looking closely at your customers, sales or the broader market. Hosted externally or in-house, it makes accessing clean, reliable data for analytics projects easier.

Pricing factors for a datamart:

• Number of data sources
• Data accessibility and ‘messiness’
• Your team’s level of capability
• Your business goals
• How much logic is required to get your desired outcome

 

Average datamart cost: $280K

 

Real-life datamart example:

With hundreds of stores, hundreds of thousands of products and millions of customers, it was difficult for our supermarket client to get a clear view of anything in their business. They wanted to change that – and, more specifically, wanted to track customer purchases across multiple loyalty programmes. A datamart was the answer.

The project began with a workshop that involved product, marketing and IT teams discussing the scope, design requirements, build process and maintenance for the datamart. The result is a centralised, accurate source of customer loyalty data that lets the business track customer activity and deliver personalised marketing based on that information. The build cost for the datamart was approximately $570k. Again, there is an ongoing operational and maintenance programme in place to ensure the data is accurate and available when needed.

 

Further reading: Get Data(s)mart: 3 reasons your business needs a Datamart

 

 

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Data cleaning costs

Data cleaning is about clarifying, sorting, and standardising your data to prepare it for analysis. The specific processes depend on the quality and scale of your data sources. Cleaning could involve matching details to build a single view of customers from different databases, removing duplicate customer details, or using external data sources to standardise information in your database. Data quality and sourcing vary across our clients, which complicates providing a uniform pricing example. The following factors are what we consider when pricing our services.

Pricing factors for data cleaning:

• The volume and location of your data
• Cleaning processes required
• The complexity of existing data sources
• What data will be used after cleaning
  

 

Further reading: How bad is your data - and is fixable?

 

 

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Predictive models, AI and machine learning costs

Predictive modelling, AI and machine learning solutions help you harness the power of your data. Predictive models use historical data and external trends to make projections about the future. AI offers endless possibilities – from tailored product recommendations to auto-generated monthly reporting.

Pricing factors for predictive modelling and AI:

• The purpose of the model or analysis
• The volume of data involved
• How complex the algorithm will need to be
• The importance of real-time capability
• Change needed within the solution over time
• Whether the model needs to be operationalised
  

 

Average predictive modelling and AI costs:: 

 

Predictive model development: $70k

AI recommendation engine build: $60k

 

Real-life AI example:

When a Datamine client wanted to change how they recommended content to customers browsing their site, we stepped in to help. The result was an AI-powered recommendation engine that learns from customer activity and interactions over time and continually updates its recommendations in response.

Instead of simply recommending articles and content based on common keywords, the engine creates individual recommendations and considers factors like the length, style and content of each piece to make relevant, up-to-date recommendations. The cost of delivering the engine and AI was approximately $185k.

 

Further reading: Datamine's Guide to Predictive and AI Modelling Process

 

 

The Datamine difference

Our pricing reflects decades of experience, an outstanding team and a pragmatic, results-driven approach.