How Can AI Solutions and Data Analytics Help Your Business?

By Lara Fox, Director at Objective

What are AI solutions and data analytics?

Businesses can use AI solutions to gain insight that can help improve business performance and efficiency. This insight can be leveraged in several ways including customer insights and personalisation for marketing purposes, improvement of operational efficiency and automation of processes. It can also contribute greatly when it comes to support with optimising sales and revenue. Predictive analytics can be used to plan for the future when forecasting trends, understanding customer behaviour and market conditions and can really help guide businesses with planning, becoming a hugely beneficial tool for decision makers.

AI and Data analytics have seen a huge advancement when it comes to technology. It means that business owners can collect relevant data and use it to make enhancements to their business and plan for a positive future.

AI Solutions and Data Analytics: what can business owners use them for?

Sales and marketing

Analytics can assist with sales performance and goals. Where it would normally take days to analyse data manually, AI and data solutions can work a lot faster, cutting out valuable business time and allowing business owners to see patterns emerging and spot trends. The data insights can then be used to gain further knowledge into clients and customer behaviour which can then be used to leverage sales and personalise marketing efforts.

Many business emails are likely already using AI and analytics tools without realising. For example, looking at the open and click through rates for their marketing emails to predict the best times to send communications to each specific business audience. It also allows business owners to create segments in marketing data, using machine learning to classify customers into segments based on their actions and profile. Natural Language Processing (NLP) can also analyse customer feedback from social media, reviews, surveys to understand and identify areas for improvement.

Financial processes

Many businesses use data automation to streamline financial processes which can help with the management of large volume financial tasks. Most commonly, this might include bookkeeping, payroll, invoices and expense management. Application Programming Interface (APIs) can link different systems together such as Xero or Salesforce enabling the systems to share data and perform analytics to produce insights. Taking this another step further, businesses can use data analytics to spot errors or inconsistencies in the data being automatically transferred between programmes, saving businesses both time and costly mistakes.

Inventory and forecasting

Businesses can use machine learning and AI for predictive analytics within inventory management. Databases and web apps accumulate data from multiple sources, adding in machine learning (ML) algorithms to forecast information. Machine learning can also be used to forecast demand and optimise inventories. This is not just for large businesses, Microsoft’s ‘Azure Auto ML’ can run 50 machine learning models on data without needing a single line of code making it accessible for SMEs too. It can be linked with Power BI to produce dashboards which allow business owners to visualise the data and use it for reporting. Using data sets, machine learning and intelligence together can help towards making data-driven decisions.

Supply chain

Algorithms and real-time data analytics can help supply chain optimisation by predicting disruptions and finding key areas of inefficiency, minimising any impact and waste of unnecessary stock and resource. Power BI can also help here with seeing the flow of goods, as well as finances and any additional information across the supply chain which can help businesses with identifying ways to help with operational performance going forward. Trend data from machinery and equipment can also help predict the lifespan of equipment, identifying failures before they occur and in addition reducing downtime and maintenance costs.

Task allocation and logistics

Automated timesheet retrieval and job allocation can be made more efficient with APIs, by collating data together within one single report. It can also be used for logistics planning, allocation of resources and distribution of staff. Route planning can also be delivered by using AI technology, using data from databases and IOT devices such as sensor data which helps automate allocations.

Despite being around for some time, there is still some lack of understanding around AI and data analytics amongst businesses and its lack of utilisation could be holding many back. With so many options to choose from, it can be difficult to understand what the tools can be used for, however, when the time is taken to gain the knowledge around these solutions it could be hugely beneficial in the long run.

Business owners don’t need to be experts in technology to get started and there are several tools out there that are available to try. Tools such as Microsoft Azure Auto ML suite are easy to use and require low or no code to get started with. Microsoft tools also come with Co-Pilot to navigate people through each step.

The first step to take is to put the data into a tool such as Power BI or a similar reporting tool to visualise the data. Once you have this data you can then start to ask it questions. Every business with an accountancy system should be able to try these solutions by connecting numbers and costs to a visualisation tool. This will enable business owners to see any useful trends and patterns emerge.

Being able to use the data combined with other tools allows business owners to truly see where their business is at and how it can be streamlined to become more efficient, to keep evolving and continue to improve.