{"id":340493,"date":"2020-10-21T06:18:34","date_gmt":"2020-10-21T13:18:34","guid":{"rendered":"https:\/\/www.genesys.com\/?post_type=blog&p=340493"},"modified":"2020-10-09T06:21:43","modified_gmt":"2020-10-09T13:21:43","slug":"ai-automates-and-simplifies-contact-centre-workforce-planning","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-gb\/blog\/post\/ai-automates-and-simplifies-contact-centre-workforce-planning","title":{"rendered":"AI Automates and Simplifies Contact Centre Workforce Planning"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text]Thoughts of artificial intelligence (AI), especially dueling algorithms, might immediately conjure up images from movies like \u201cThe Terminator\u201d or \u201cThe Matrix\u201d \u2014 where these technologies work to enslave humanity. However, even now, they\u2019re used in very much the opposite way: finding opportunities to free us from constraints and give us more flexibility when \u2014 and how \u2014 we work.<\/p>\n

Over the next few years, companies using AI-based forecasting and scheduling tools<\/a> might build more goodwill with their employees as they allow them to work around other obligations, choosing the shifts they want to work and take off. And as more employees work from home and often wear many hats at work, streamlining employee scheduling isn’t a luxury \u2014 it\u2019s a requirement to retain workers and keep them motivated. However, a contact centre manager or workforce planner have to balance this requirement with many other competing tasks.<\/p>\n

Contact centres are staffed to ensure there are enough agents to handle the volume of inbound and outbound interactions. Managers have to see how staffing and interaction forecasts will affect handle times, first-call resolution, Net Promoter Score (NPS) and other metrics that they measure against. They also have to take into account employee vacations or scheduled time off, as well as holidays and sick leaves, to be sure they’re adhering to labor laws when creating work schedules. This all can be overwhelming.<\/p>\n

Manual Planning and Spreadsheet Failure <\/strong>
\nTo figure out when employees should work, a planner has to gather data about previous interactions. Looking ahead, they\u2019ll need to adjust for expected volumes around holidays or planned sales. Then they can build their forecast for interactions with mathematical models. Once that\u2019s done, they need to review and verify the models to make sure they\u2019ll actually work. Next, the planner applies a staffing forecast and builds a schedule for individual employees or queues. Finally, manual adjustments are made to account for individual employee preferences.<\/p>\n

Many planners still rely on spreadsheets for planning \u2014 not because that\u2019s the best method, but because they provide the flexibility needed to consider employee preferences and different requests. Planners must be flexible and make manual changes. However, as contact centres grow and complexities increase, these manual systems can fall apart.<\/p>\n

The forecasting part of the process tends to be the easier because the planner hands off the task to a mathematical model. New systems, such as in the Genesys Cloud\u2122<\/sup>\u00a0platform<\/a>, run dozens of different algorithms concurrently, including AI-based models that improve accuracy. The competition is run against segments of historical data until the winning method is selected. However, these systems don’t stop there. The competition is run again every time a forecast is created, in case different methods are a better match because of external factors that affect predictions.<\/p>\n

Setting up AI-Based Forecasting <\/strong>
\nMuch of the hard work involved with setting up AI-based forecasting<\/a> requires collecting and preparing the data for consumption by a forecasting machine. Fortunately, systems that automate the collection of time-series data, ensuring that it’s properly labeled and formatted, can reduce much of that effort. The next step is for these systems to remove outliers that can affect the forecast \u2014 identifying anomalies versus ones that must be considered, such as seasonal effects. Once these two steps are complete, you can use pattern recognition to look for positive and negative trends and seasonality. Then apply a bake-off of AI tools and different forecasting methods to find a winning method.<\/p>\n

Once you have a reliable forecast, you can confidently apply different schedules against it and simulate how employees\u2019 time off preferences will affect metrics like average handle time and NPS. You can also implement system-based policies to automatically grant time-off requests or allow for shift trading \u2014 without needing a manager to intervene.<\/p>\n

Beyond the benefits of better, more predictable performance against KPIs, contact centres that implement AI-based forecasting systems will change the relationships between employees and managers<\/a> \u2014 for the better. For example, managers can focus less on reviewing whether an employee can take time off to help a family member and focus more on coaching and improving service offerings. This improves work satisfaction and creates happier employees who, in turn, pass this empathy onto your customers.<\/p>\n

Find out how\u00a0AI can improve contact centre and agent performance<\/a>\u00a0in this brief.[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text]Thoughts of artificial intelligence (AI), especially dueling algorithms, might immediately conjure up images from movies like \u201cThe Terminator\u201d or \u201cThe Matrix\u201d \u2014 where these technologies work to enslave humanity. However, even now, they\u2019re used in very much the opposite way: finding opportunities to free us from constraints and give us more flexibility when \u2014 […]<\/p>\n","protected":false},"author":235,"featured_media":335862,"template":"","tax_priority":[],"tax_blogtype":[17751],"tax_blogcategory":[15939,13571],"tax_contenttheme":[],"tax_bundle":[],"tax_contenttheme2":[],"tax_capability_sitewide":[],"tax_products_programs":[],"tax_buying_job":[],"tax_buyer_persona":[],"tax_sector":[],"tax_segment":[],"class_list":["post-340493","blog","type-blog","status-publish","has-post-thumbnail","hentry","tax_blogtype-genesys-en-gb","tax_blogcategory-ai-and-machine-learning-en-gb","tax_blogcategory-workforce-engagement-en-gb"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog\/340493","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/users\/235"}],"version-history":[{"count":4,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog\/340493\/revisions"}],"predecessor-version":[{"id":343831,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog\/340493\/revisions\/343831"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/media\/335862"}],"wp:attachment":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/media?parent=340493"}],"wp:term":[{"taxonomy":"tax_priority","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_priority?post=340493"},{"taxonomy":"tax_blogtype","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_blogtype?post=340493"},{"taxonomy":"tax_blogcategory","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_blogcategory?post=340493"},{"taxonomy":"tax_contenttheme","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_contenttheme?post=340493"},{"taxonomy":"tax_bundle","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_bundle?post=340493"},{"taxonomy":"tax_contenttheme2","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_contenttheme2?post=340493"},{"taxonomy":"tax_capability_sitewide","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_capability_sitewide?post=340493"},{"taxonomy":"tax_products_programs","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_products_programs?post=340493"},{"taxonomy":"tax_buying_job","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_buying_job?post=340493"},{"taxonomy":"tax_buyer_persona","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_buyer_persona?post=340493"},{"taxonomy":"tax_sector","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_sector?post=340493"},{"taxonomy":"tax_segment","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_segment?post=340493"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}