Data enrichment AI for an efficient order-to-cash process

Incorrect or outdated data increases your business process costs and the risk of data breaches – for example, if it is not possible to obtain consent for updated data protection regulations due to incorrect contact details.

However, according to an IDC survey, the continuous optimisation of data quality is a key challenge for 37% of German companies. The complex data maintenance exceeds the capabilities of existing legacy systems, among other things.

Many teams also lack a 360° customer view, from marketing communication to receivables management. This limited view has serious consequences for the order-to-cash process (O2C) in particular. The O2C process thrives on information provided by customers. If inconsistencies occur or data is incomplete, this leads to

• incorrect invoices,
• poor payment rates,
• long outstanding amounts,
• dissatisfied customers, and
• a long-term deterioration in cash flow.

Data enrichment, also known as data enhancement, is a decisive step towards improving data quality and meeting customer needs in a targeted manner. This refers to the process of improving and expanding data sets, for example through data enrichment services such as adding relevant information or data validation.

The result: Data enrichment makes it possible to optimise the quality and informative value of data. This provides companies with a sound basis for better decisions, more efficient business processes and strong customer relationships. You can achieve these goals with Data Enrichment AI from collect.AI.

The Future

With the Data Enrichment AI, you save internal resources that would otherwise be required for data enrichment and verification. collect.AI allows you to delegate the process of data enrichment to an AI technology that has been trained with more than 100 million transaction data from EU enterprises in various sectors.

Data Enrichment AI shows its strength above all in the order-to-cash process: The AI-based tool is one of four AI assistants on collect.AI’s order-to-cash platform. This enables companies to handle the entire life cycle of a receivable end-to-end, fully digitally and AI-optimized.

Data Enrichment AI continuously increases the efficiency of your communication, transaction and payment processes. It ensures that customer contact data is always up to date. At the same time, it enables an increase in the approval rates for legally binding advertising consents and direct debit mandates.

Data Enrichment AI supports your company in

• improving your data quality
• continuously increasing the efficiency of your O2C process
• conserving resources during data validation
• using public databases or other sources
• better understanding your customers and their behavior and individual needs
• strengthening customer relationships in the long term
• maintaining compliance and data protection
• relieving the accounts receivable department
• improving the receivables management process

The AI assistance also optimises the payment rate: collect.AI customers use Data Enrichment AI to collect 90% of their receivables before they are due and therefore reduce their process costs in the O2C process by an average of 30%.

DATA SUBMISSION IN PRACTICE

Companies with 5,000 to 20,000 or more recurring receivables per month use Data Enrichment AI. These include regional energy suppliers who need to effectively digitise their traditional O2C processes in order to retain customers who are willing to switch. They must offer modern and simple payment processes and at the same time ensure their customers’ ability to pay in times of rising energy prices.

Data enrichment with AI improves the necessary database:

• Data validation when ordering: Address validation is carried out by external data sources when the tariff is concluded. This ensures, for example, that the desired tariff is available at the address and that invoices are addressed correctly later.

• Support for receivables management: If a customer has payment difficulties, the customer data can be enriched with relevant information from debtor databases. For example, a statement of assets can be used to check creditworthiness in order to take measures to restore payment.

• Personalised support: The enrichment of customer data with historical consumption data and demographic information makes it possible to identify the optimal tariff. This increases customer satisfaction and willingness to pay.